❌

Visualização normal

Antes de ontemCybersecurity News
  • ✇Security | CIO
  • What hundreds of Microsoft 365 deployments have taught me about cybersecurity
    I’ve spent more than 20 years running IT and security for organizations across California and around the country. Over that time, my team has taken over, deployed and cleaned up Microsoft 365 environments for law firms, commercial real estate companies, manufacturers, healthcare practices and schools. We’ve supported more than 7,500 users along the way. After working with that many tenants, the biggest takeaway is simple: most Microsoft 365 security problems aren’t
     

What hundreds of Microsoft 365 deployments have taught me about cybersecurity

15 de Outubro de 2026, 06:00

I’ve spent more than 20 years running IT and security for organizations across California and around the country. Over that time, my team has taken over, deployed and cleaned up Microsoft 365 environments for law firms, commercial real estate companies, manufacturers, healthcare practices and schools. We’ve supported more than 7,500 users along the way.

After working with that many tenants, the biggest takeaway is simple: most Microsoft 365 security problems aren’t especially sophisticated. Usually, a control was never turned on, only covered part of the organization, or somebody made an exception years ago to keep something working and it was never revisited. Microsoft gives organizations a lot to work with. More often than not, the problem is how the environment was configured and maintained, not a lack of security tools.

These are the issues we run into most often and the areas I’d look at first.

Most environments are still running on their original settings

When we first assess a tenant, we often find that nobody has really gone back through the security settings since it was originally set up. MFA may cover some users but not everybody. Legacy authentication is still allowed. There are too many global administrators. Nobody is quite sure when audit logging was last reviewed.

Last year we took over a 45-person medical practice that had been on Microsoft 365 for almost a decade. We found nine global administrator accounts, including two belonging to employees who had left years earlier. MFA was enabled, but only for the physicians. The front desk and billing staff, who handled the most patient data, had none.

Usually, nobody intentionally ignored security. The company grew, email kept working, people got busy and the original setup stayed in place. We see this constantly. Something working every day doesn’t necessarily mean it is configured the way you would want it configured today.

For a smaller organization without dedicated identity management, Microsoft’s security defaults are a good place to start. They require MFA and block legacy authentication without adding another license. As the environment gets more complex, and assuming the licensing supports it, conditional access gives you much more control over who can sign in, from where and from what device.

MFA is the starting point, not the finish line

At this point, most business leaders know MFA needs to be enabled. What still gets missed is the idea that turning it on somehow finishes the job.

Attackers have adjusted to MFA. Push fatigue attacks still work because eventually somebody may approve a prompt just to make it stop. Adversary-in-the-middle phishing kits can steal a live session token and get around standard MFA altogether. OAuth consent phishing is another issue: a user can be tricked into approving a malicious application through what looks like a legitimate Microsoft or Google consent screen. At that point, changing the user’s password may not even remove the access.

A few months ago, a paralegal at a law firm we support clicked what looked like a shared document from opposing counsel and signed in through a convincing Microsoft login page. She had MFA and completed the prompt, but the page was relaying her session in real time. Within minutes the attacker was in her mailbox from overseas. We caught it through an impossible-travel alert, revoked the session and reset the account before any client data left.

There are a handful of things we normally tighten up here. Use number matching in the authenticator app. Put administrators and other high-risk users on phishing-resistant methods such as FIDO2 keys or passkeys. Don’t let users approve any third-party application they want. And where licensing allows it, use conditional access so a password or stolen session by itself isn’t enough to get into the environment.

Legacy authentication remains the open side door

This one still surprises a lot of executives. They know MFA is enabled and assume that means every sign-in is protected. That isn’t always the case. Legacy protocols such as POP, IMAP and older SMTP connections don’t support MFA, which can leave a path around the control.

Microsoft has retired a lot of legacy authentication already, but we still come across exceptions that were left in place for an old scanner, a line-of-business application or sometimes one user’s device. Find out what actually still needs it, fix or replace that dependency, and close the exception. We have seen organizations leave a security hole open for years because of one multifunction printer.

Licensing determines what security is actually available

Licensing is another area that causes a surprising amount of confusion. Two plans can sound almost identical while giving the IT team very different security capabilities. Office 365 E3 and Microsoft 365 E3 are a good example: Microsoft 365 E3 includes Intune device management and Entra ID P1 conditional access, while Office 365 E3 does not.

We once had a healthcare client with a departing employee who kept a company laptop with patient records on it. Leadership assumed we could lock or wipe it remotely. We couldn’t. They were licensed for Office 365 E3, not Microsoft 365 E3, so the device had never been enrolled in Intune. We got the laptop back, but only after several uncomfortable days, and they upgraded their licensing the following week.

So before putting together a security roadmap, I would first confirm exactly what licenses are in place and what those licenses actually include. We’ve had more than one situation where leadership assumed a security capability was available simply because the Microsoft plan name sounded similar.

Email is still where most incidents begin

For us, email is still where a large percentage of incidents start. It may be a credential phishing page, a malicious link, or an inbox rule created after somebody has already gained access to the account.

Business email compromise is especially dangerous because the attacker often doesn’t do anything obvious right away. They get into a mailbox, create forwarding rules or hide replies, and wait. The goal may simply be to sit there until an invoice or wire transfer gives them an opportunity. According to the FBI’s Internet Crime Complaint Center, reported cybercrime losses reached $16.6 billion in 2024, and BEC continues to be one of the more expensive categories of fraud.

At one law firm, an attacker compromised a mailbox and created a rule that quietly moved any email containing “invoice” or “wire” into an unused folder. Two weeks later, a client received updated wiring instructions for a settlement payment from what looked like the firm’s own account. The client called to confirm before sending the funds, and that phone call is the only reason the money didn’t go out.

We normally put several controls around this. Block automatic forwarding to outside domains. Alert when inbox rules are created or changed. Most importantly, have an internal policy that any change to payment or wiring instructions gets confirmed verbally using a phone number you already know. That last one has nothing to do with cybersecurity software, but it can stop a very expensive mistake.

Privileged accounts need their own standard

The global administrator list should be short, intentional and documented. We also don’t want somebody using the same privileged account to read email and browse the web all day. It sounds basic, but we correct this all the time.

Use separate administrative accounts, put phishing-resistant MFA on them, and use just-in-time elevation where the licensing supports it. A compromised global administrator account gives an attacker an enormous amount of control, so we treat those accounts differently from a normal user.

Logging only helps if it’s configured before you need it

When something does happen, we immediately need to know what happened, when it started and what the attacker touched. If the right logging wasn’t enabled beforehand, or the retention period is too short, sometimes you simply can’t reconstruct the whole incident after the fact.

I’d much rather configure logging on day one and never use it than reconstruct an incident from partial data. CISA’s Secure Cloud Business Applications (SCuBA) baselines are an excellent, free reference for which settings to enable and how to verify them.

Where I would start

Microsoft 365 can be a very secure platform. In most environments we take over, the issue isn’t that Microsoft was missing some critical security feature. It’s usually a relatively short list of settings, permissions and exceptions that accumulated over time.

If I were reviewing an environment this quarter, these are the four things I would verify first:

  1. Administrative access is minimal and protected with phishing-resistant MFA.
  2. Legacy authentication is blocked.
  3. MFA covers every user, and users can’t approve third-party apps unchecked.
  4. You would have complete logs if something happened tomorrow.

Those four checks won’t solve every security problem, but they eliminate a lot of the issues we repeatedly find when we’re brought in to assess an environment or clean up after an incident.

  • ✇Security | CIO
  • FBI removes contractor working with its PeopleSoft system after data breach, says report
    The US Federal Bureau of Investigation has removed an Accenture contractor working on its PeopleSoft installation over their role in a data breach that exposed personal details of FBI employees, Reuters reported Monday. This is the first time that anyone other than the hacker group Shinyhunters that claimed responsibility for the breach has linked it to PeopleSoft. “To date, our review has determined that the incident occurred as the result of a security failure of a
     

FBI removes contractor working with its PeopleSoft system after data breach, says report

6 de Outubro de 2026, 15:29

The US Federal Bureau of Investigation has removed an Accenture contractor working on its PeopleSoft installation over their role in a data breach that exposed personal details of FBI employees, Reuters reported Monday.

This is the first time that anyone other than the hacker group Shinyhunters that claimed responsibility for the breach has linked it to PeopleSoft.

“To date, our review has determined that the incident occurred as the result of a security failure of a platform managed by a third-party organization — after a contractor failed to implement a security patch explicitly issued to secure the platform,” FBI Cyber Division Assistant Director Brett Leatherman told Reuters. “As such, the FBI has removed the contractor and taken all necessary steps to both mitigate any further risk and protect our workforce.”

Accenture was the third-party organization Leatherman would not identify, “two sources familiar with the matter” told Reuters.

As for the unnamed, unpatched platform, Reuters’ sources said that was Oracle PeopleSoft, the HR system that hacker group ShinyHunters claimed to have exploited in last month’s break-in.

This article first appeared on CSO.

  • ✇The Cloudflare Blog
  • Follow the thread: a new dashboard to investigate account abuse Nicole Justus
    Traditionally, preventing online fraud relied on point-in-time proof of identity: enter the correct password, complete a biometric verification, or pass a liveness check, and gain access. To defeat these controls, fraudsters had to steal credentials and other identity evidence from a real user, which was difficult to execute and scale. Today, widespread access to AI enables fraudsters to fabricate or imitate legitimate identities by combining exposed credentials with synthetic media designed to
     

Follow the thread: a new dashboard to investigate account abuse

2 de Outubro de 2026, 10:00

Traditionally, preventing online fraud relied on point-in-time proof of identity: enter the correct password, complete a biometric verification, or pass a liveness check, and gain access. To defeat these controls, fraudsters had to steal credentials and other identity evidence from a real user, which was difficult to execute and scale. Today, widespread access to AI enables fraudsters to fabricate or imitate legitimate identities by combining exposed credentials with synthetic media designed to evade identity verification. Consequently, identity checks are no longer sufficient as they capture a moment in time. Even when someone passes a check, it does not mean the account itself can be trusted.

One convincing interaction can be faked. A consistent pattern of legitimate behavior is much harder to manufacture. Modern fraud prevention must move beyond stateless decisions toward a stateful trust model. Traditional identity verification asks, “Can this person pass the check right now?” A stateful approach additionally asks, “Does it fit what we know about this account and its established behavior?” At Cloudflare, trust is continually earned and reassessed at each interaction against historical behavioral, network, and device patterns.

Cloudflare’s Account Abuse Protection (AAP) creates stateful account overviews to help website owners detect and investigate abuse across login and signup activity. Customers configure an identifier from their existing login or signup flow, such as an email address, username, or phone number. Cloudflare cryptographically hashes that value to create a privacy-preserving, per-domain Hashed User ID. Within AAP, a Hashed User ID represents an account and anchors its activity. With each login or signup, AAP adds the event and relevant network and device signals observed at Cloudflare’s edge. Over time, this accumulated history establishes context for the account’s typical behavior, making meaningful deviations easier to identify and giving fraud teams (i.e., the designated personnel for Security Intelligence, Investigations, Trust & Safety, or Risk & Compliance) a stronger foundation for investigation.

Today, we are introducing a new fraud dashboard for Account Abuse Protection, available first to Early Access customers. The workspace brings together account overviews built from activity observed across a website’s configured login and signup flows. It allows fraud analysts to view their entire user population, identify suspicious trends, and move from aggregate activity patterns into specific account investigations.

Dashboard overview: From population visibility to individual account depth

The dashboard is designed as an investigative funnel. When a suspicious event has been identified, fraud teams can review the account population overview to understand the scale and shape of suspicious patterns without needing to investigate every account individually.

Teams can review total login and signup volume, see how many accounts generated those events, and view the unique IP addresses and devices observed across those accounts. Country and ASN breakdowns provide additional context about where the activity was observed.

The account population overview helps fraud teams answer questions such as:

  • Did login or signup volume change unexpectedly?
  • Are failed logins or leaked credential matches increasing?
  • Which accounts show the highest login failure rates?
  • Are events concentrated in particular countries, ASNs, or times of day?
  • Did a sudden signup increase coincide with shared characteristics?
  • Which accounts may have been affected by the attack?

From there, fraud teams can determine the campaign’s scope, prioritize accounts for manual review, reconstruct what happened within those accounts, and decide how to respond.

AAP in Action: Investigating a credential stuffing attack

Consider a fraud prevention or security analyst team investigating unusual login activity. The team opens the Account Abuse Protection dashboard to determine how broadly a credential stuffing attack may have affected its users. The dashboard allows analysts to investigate from total event traffic all the way to individual accounts that warrant review. The individual account view provides the history and context needed to reconstruct what happened and determine the appropriate response.

1. Spot the anomaly. The investigation begins in the account population overview, where the fraud team determines whether suspicious activity is isolated or part of a broader campaign. An increase in failed login activity prompts the team to examine Leaked credential check results on login events.

In this example, a leaked credential summary shows that approximately 2.4K events produced a leaked username or password result, compared with 11.7K events where credentials were classified as clean. This pattern is an investigative lead, not confirmation that every affected account was compromised.

The team can now focus on accounts associated with leaked credential matches. Are multiple accounts connected to the same IP addresses or ASNs? Does an individual account suddenly appear across an unusually high number of IP addresses? These relationships help define the potential scope of the credential stuffing campaign and identify the accounts that should be prioritized for review. Analysts can also look at the dashboard for concentration across particular IP addresses, ASNs, locations, or devices.

2. Narrow the field of investigation.  Filters help narrow the account population to specific accounts with the most concerning combination of signals and identify which ones warrant manual review.  For example, filters can be set to look at accounts with at least three failed logins, at least three leaked credential matches, and observed from at least five unique IP addresses.

From this filtered cohort, analysts can select the specific Hashed User IDs, whose recent activity requires the most urgent attention.

3. Investigate an account. Analysts can review login attempts, identify new devices or locations, and reconstruct how activity unfolded. Using the event table, they can compare earlier clear events with later suspicious activity, pinpoint when the pattern began, and determine whether it was a single event or a series of repeated attempts. Each event includes a Ray ID that analysts can use to look up associated information in Security Events.

4. Decide how to respond. If review confirms that an account was compromised, analysts can begin their established recovery process. They can also use the Hashed User ID in a WAF rule to challenge or block future requests associated with it.

A closer look at an individual account

An individual account view provides another layer of depth for investigation. It summarizes the login and signup activity observed for that account, including its login success rate, leaked credential matches, and most frequently associated networks, locations, and devices. Analysts can then examine the individual events behind the account summary. Each event includes its timestamp, Ray ID, and any mitigation applied. A Cloudflare Ray ID is an identifier given to every request that goes through Cloudflare, that teams can use to look up associated information in Security Events.

This detailed summary and event log helps answer questions such as:

  • Was this a single login event or part of a series of repeated attempts?
  • Did that login introduce a new country, network, IP address, or device?
  • Did the user’s behavior change afterward?
  • What happened before and after a suspicious event?
  • Did concentrated login activity follow shortly after signup?
  • Did signup and subsequent login activity use different network or device characteristics?

Viewed together, these signals help fraud teams determine whether an account requires recovery, restricting access, or another response, depending on how the customer wants to treat these accounts. When there is enough evidence, analysts can use the Hashed User ID in a WAF rule to challenge or block future requests associated with that identifier.

Designed to minimize unnecessary data exposure

Account Abuse Protection provides account level context while also giving Cloudflare customers control over who can access account information. This launch introduces two new roles (i.e., access levels): Account Abuse Protection and Account Abuse Protection PII. The Account Abuse Protection role controls access to the dashboard, while the Account Abuse Protection PII role controls access to additional account-level PII (e.g., email) . We encourage Customer Administrators to assign these roles on a need-to-know basis, based on what each team member needs to investigate.

The Account Abuse Protection PII role is also required to create or update Logpush jobs containing PII. Separating these permissions helps customers apply least privilege access to both dashboard and data export workflows.

Take the next step in account protection today

The new dashboard is available first to Account Abuse Protection Early Access customers. Bot Management Enterprise customers interested in these capabilities can sign up for Early Access. Prospective Bot Management Enterprise customers can use the same form to contact our team.

Bot detections help customers understand whether activity is automated. Account Abuse Protection adds account level overview to help fraud teams investigate whether login and signup activity appears authentic and consistent with legitimate use. Together, these capabilities help website owners address automated and human-driven abuse across account creation and login.

  • ✇The Cloudflare Blog
  • Enforce positive security with Cloudflare Application Profiles Daniele Molteni · Zhiyuan Zheng
    Today, we are launching Application Profiles, a seamless way to enforce a positive security policy. By analyzing the structure and format of HTTP requests and identifying deviations, Cloudflare can help you significantly reduce the attack surface area.Every customer we speak to wants to know how we can protect them from attacks that use frontier AI models. This has become the number one priority for anyone working in security. Large language models (LLMs) allow even non-technical people to launc
     

Enforce positive security with Cloudflare Application Profiles

29 de Setembro de 2026, 10:00

Today, we are launching Application Profiles, a seamless way to enforce a positive security policy. By analyzing the structure and format of HTTP requests and identifying deviations, Cloudflare can help you significantly reduce the attack surface area.

Every customer we speak to wants to know how we can protect them from attacks that use frontier AI models. This has become the number one priority for anyone working in security. Large language models (LLMs) allow even non-technical people to launch attacks with a single prompt. LLMs can generate malicious payloads, test known techniques, and probe applications autonomously by mutating their tactics based on the feedback from the application or the Web Application Firewall (WAF). 

Our tools have changed to stay a step ahead of the attackers. Managed WAF rules and machine learning-based detections remain essential for detecting techniques such as SQL injection, cross-site scripting, remote code execution, and new CVEs, including many variations of those attacks. The answer can’t simply be “patch faster”: this is not sustainable, and it doesn’t work if you haven’t completely mapped your vulnerabilities.

What if you could learn what good requests look like by analyzing your traffic structure? Instead of looking only for requests that resemble known attacks, we could allow only requests that conform with what we expect. By doing this, we’d dramatically reduce the attack surface area. For example, if the search field in your query doesn’t expect special characters, we can only accept alphanumeric strings. This would already prevent a vast library of known attacks.

But we don’t stop here. Once we have learned the structure and format of your HTTP requests, we can infer the goal of each operation and then understand what the application ultimately does. With this information, we can identify and prioritize the most critical and vulnerable operations and fields you should take care of first.

Cloudflare already supports positive security for APIs through Schema Learning and Schema Validation. We are now extending this protection to web applications through Application Schema Profiles. You onboard an application, we learn its profile, and then we start to deploy an always-on detection that identifies non-conformity. All automated and enriched by powerful analytics.

We are opening a closed beta to invited Enterprise customers without API Security; customers with API Security already have access.

Validating requests based on learned profiles

Schema Profiles periodically learn the expected request structure from observed traffic. After a profile is available, an always-on validation layer is automatically deployed on live traffic. For every request, the detection evaluates whether it conforms or not with the profile, and it adds the result as metadata, augmenting the information already associated with the request. The signal does not take action by itself: customers can analyze past traffic in Security Analytics and decide where enforcement is appropriate and create Security Rules to block non-conforming requests. Requests to operations without a profile are not classified by this feature.

Unlike Managed Rules, failing validation does not require a request to match a known attack signature. A value outside an expected range, an unknown enum value, an invalid universally unique identifier (UUID), or unexpected characters — all can be identified because they differ from the learned profile.

For example, consider the following operation: 

www.example.com/shop/2dbda2e7-cfc9-448d-9465-799d2e6ff363/inventory?product_id=938062541

Below we describe the learning process, which evaluates only the structure and format of the request. When enough traffic has been observed, we learn that the path expects a UUID variable and that product_id is an integer and what its boundaries are. When product_id contains a string, it will be flagged as a violation. Similarly, Cloudflare can identify malformed UUID values and, when the customer enables enforcement, prevent non-UUID input from reaching the corresponding handler. These simple filters reduce the range of inputs an attacker can send, preventing the vast majority of typical attack vectors, such as SQL injection, cross-site scripting, remote code execution and more. 

Non-conforming does not always mean malicious. An application release, a new client, or an unusual but valid request may also introduce a difference. We recommend starting in observation mode, so customers can review a profile's effect before enforcement.

Learn the expected structure of requests

To determine the anticipated request structure for a web or API application, Schema Profiles routinely analyze observed traffic. Each profile may include the following, depending on the application traffic:

  • Path variables 
  • Query parameters
  • Headers and cookies
  • Body structure (JSON body or form-encoded)

For each field, the system learns its data type (integer, string, boolean, arrays, UUID or enum) and constraints such as numeric ranges, short enumerations, string lengths, and character classes.

Learning applies to operations that customers select for profiling. In Web Assets, an operation is Cloudflare's term for an operation identified by its HTTP method, hostname pattern, and path pattern. Web Assets continuously discovers operations and lists them under Web Assets > Operations. Customers can also add operations manually. Profiling doesn’t automatically start for discovered operations, while manually created operations do trigger profiling when created. For discovered operations, the customer must intentionally select Learn profile from the operation's overflow menu. 

After profiling is enabled, Cloudflare collects qualifying traffic and runs learning automatically once a week for each zone, using the most recent successful traffic. An operation needs at least 1,000 requests that received a 2xx response in the previous seven days to learn fields, and at least 10,000 to learn data boundaries. Successful requests can include bots and scanners, so customers should review a learned profile before enforcing it. Our roadmap includes allowing customers to trigger learning on demand and excluding automated traffic.

Once learned, profiles can be reviewed by selecting View details of the operation and finding the learned schema in the Security overview panel. If a learned schema is not shown, Cloudflare is still collecting data for the profile. Customers can also export the profile as an OpenAPI v3 schema file.

Learned profiles update each week as application traffic changes. New fields are added and fields that are no longer observed are removed, so validation tracks how the application changes. Customers can pin and save the learned schema by downloading the learned schema and uploading it to Schema Validation.

Review before you block

Security Analytics now includes a new Profile Analysis tab. Customers can select a validation profile and see traffic trends, including how many requests did not conform to the learned profile during the previous seven days. 

Customers can review the conforming and non-conforming traffic. They can drill into violations and review sampled logs to see where the violation occurred, which field was affected, and why it failed validation. Violations are classified into ten reasons, including type mismatches, values outside a learned range, and invalid formats.

Once a team understands the effect, it can use Security Rules to act on the signal. A rule can cover an entire application or be limited to selected paths, operations, or fields. Teams control where to monitor and where to block.

Positive security for web and API traffic

Traditional WAF learning modes can build detailed positive-security policies, but they often require operators to review suggestions, stage changes, and maintain policy entities. Cloudflare Schema Profiles expose validation as a request field cf.schema_validation.learned.violated, allowing customers to combine it with request properties, Bot Score, Attack Score, and other signals in a single Security Rule. By creating simple rules, teams can combine detections and define precisely when Cloudflare should take action.

Two other classes of fields are available to create more targeted rules. First, there are fields that collect where the violation occurred. For example, based on our initial example, if the value of product_id query parameter does not conform with the profile, the following field will be populated cf.schema_validation.uploaded.query.violated_parameters = ["product_id"]. This allows customers to create rules that enforce positive security only on specific fields or exclude them from the enforcement.

The second class of field collects new parameters that are not present in the profile. This is useful when you want to handle requests with new parameters (e.g. when deploying a new version of your application), or restrict your posture even further by blocking any parameters that were not detected or defined in the past.

Use case

Field

Location values

Example

Identify where in the request the violation occurred

Array up to 20 items

cf.schema_validation.learned.[location].violated_parameters

query,path,headers,cookies,body

cf.schema_validation.learned.query.violated_parameters = ["product_id"]

Identify whether an undeclared parameter is seen in the request 

Array up to 20 items

cf.schema_validation.learned.[location].undeclared_parameters

query

cf.schema_validation.learned.query.undeclared_parameters = ["adminMode", "utm"]

Coming up: critical field analysis, how we help you roll out positive security

Even with a flexible enforcement design, customers tell us that deploying a positive security policy is operationally complex. A large application can have thousands of operations with tens of thousands of fields. But not all operations and fields carry the same risk. Contextualization and prioritization helps security teams roll out positive security in a controlled and confident manner.

LLMs can help contextualize learned profiles to provide additional insight. For web applications, paths and field names are usually self-explanatory, thus semantic. For example, we piloted running a model hosted on Workers AI across four random applications’ learned profiles. The model successfully identified the link between clientId and account_number across two applications of a system, as well as the common dependency of using One-Time Password (OTP) for enhanced authentication. Highlighting this context enables security teams to prioritize actions such as configuring Rate Limiting Rules to defend against account-focused brute force attacks.

These LLM-powered insights will be accessible directly within the dashboard alongside each operation in Web Assets. Before executing a one-click deployment, teams can evaluate rule recommendations designed to secure these key fields, backed by mitigation simulation using past traffic to gain confidence.

Beyond contextualizing operations with semantic insights and risk indicators, we are developing additional metrics to order operations using historical request trends and signals. This enables security teams to focus mitigation efforts on the highest-priority operations first, including:

  • Data loss: upward trend of unusual increased data transfer
  • Reconnaissance activity: high count of unknown parameters
  • Business criticality: total volume of traffic correlated with the unique session IDs served

What’s available today

Customers with API Security already have access, given that this is an extension of Schema Learning and Schema Validation. We are opening a closed beta to customers without API Security who can test Schema Profiles on production web application traffic, meet with the product team, and provide detailed feedback on profile accuracy, analytics, and enforcement controls. Access is by invitation and does not imply future plan availability. If you are not an API Security customer and want to get access, contact your account team.

The feature supports paths, query parameters, headers, cookies, JSON request bodies, and form-encoded request bodies. Profiles can validate integers, strings, UUIDs, arrays, and enums containing up to three values. Multipart forms, GraphQL, and XML are not supported at this time.

Schema Profiles validate every value when a parameter name is repeated, but they do not enforce parameter uniqueness. They also do not learn and enforce required parameters or block a request solely because it includes a new parameter.

Get ahead of zero-days

Our idea for Application Profiles does not stop at validating request structure. The same workflow can learn other characteristics of what an application expects (such as ASNs or JA4s), explain when traffic deviates from them, and give security teams confidence in defining what “good” looks like. With a Proactive Security workflow, we help security teams get ahead of zero-days!

Harness’ Rahul Sood: AppSec in the Agentic Era Is About More Than Code. It’s About Authority

23 de Setembro de 2026, 06:31

The Cyber Express Rahul sood Interview

AI is changing how software is built, tested, and secured, but the speed of development is also creating new challenges for application security teams. As AI coding tools and autonomous agents increase the volume and pace of software development, security teams are facing a growing gap between vulnerability detection, prioritisation, and remediation. In this interview with Rahul Sood, GM, Application Security at Harness, we explore how AI application security is evolving as developers increasingly rely on AI-assisted coding and software agents. Sood discusses why traditional checkpoint-based security processes are struggling to keep pace, how organisations can bring security controls closer to the point where code is created, and why automated vulnerability remediation could become increasingly important. The conversation also examines the security risks associated with AI agents that can access repositories, APIs, infrastructure and production environments. Sood explains why identity, least-privilege access, observability and human oversight will become increasingly important as software gains greater autonomy, and why application security will need to address not only what software contains, but also what autonomous systems are authorised to do. Read the full interview below for Rahul Sood’s perspective on the changing application security landscape and the security challenges emerging as AI takes a more active role in software development.

The Cyber Express: What is the biggest change AI is bringing to the application security landscape?

Rahul Sood: The biggest change is speed. We have spent years making software development faster, and AI has accelerated that dramatically. Security processes, however, haven't accelerated at the same rate. Most security scanning is still triggered at fixed checkpoints - a pull request, merge, or nightly build - designed for a world where a developer produces a handful of meaningful commits a day. LLM-based scanning can potentially find 10x more vulnerabilities vs. traditional scanning tools. That sounds great, but it also means the discovery side of the pipeline is now outpacing triage and remediation even more than it already was. That's where AI agents also become part of the answer, not just part of the problem. An agent can identify something and begin acting on it in close to the same motion, which is exactly the kind of compression this gap needs. The challenge is making sure that speed is matched with the right judgment and oversight.

The Cyber Express: AI coding assistants can introduce insecure code at scale. What should teams be doing differently?

Rahul Sood: The instinct is to assume AI-generated code is introducing more vulnerabilities because AI is somehow worse at writing secure code. I don't think that's the real story. Even if AI wrote code exactly as securely as a person does, the sheer volume of code now being produced would still overwhelm the way most organisations scan for issues today. Scanning at the commit is already struggling to keep pace, as we discussed. The next step is pushing security further left than that, into the coding tool or agent itself, so secrets detection, dependency checks, and application security testing happen as the code is being written, and those same controls continue through build and deployment rather than starting over at each stage. You need both: controls at the point of creation to cut volume, and a verification layer that confirms what's really going out the door. I expect the industry to move from automated detection towards much more automated resolution. Systems will increasingly identify an issue, establish whether it is actually exploitable, create a potential fix, verify that fix and then bring a developer in where judgement or approval is required. Human oversight remains important, but it should be concentrated on decisions where human judgement adds value rather than every mechanical step surrounding them.

The Cyber Express: What new security challenges emerge when AI agents can make decisions and take actions?

Rahul Sood: A coding assistant can suggest something insecure, but there are usually other controls between that suggestion and production. An agent with access to APIs, repositories or infrastructure is different. It has been given authority to act. That makes identity, permissions and the scope of that authority fundamental security questions. One principle I think will become increasingly important is that autonomy should be proportional to the consequence of the action. The more consequential and difficult an action is to reverse, the stronger the controls around it should be. More broadly, enterprises will need a security architecture for non-human actors: distinct identities, least-privilege access, short-lived credentials, complete auditability and clear boundaries around which decisions an agent can make independently. The important question isn't how autonomous an agent is. It's what we've given it the authority to do, where the boundaries are, and what happens when it gets a decision wrong.

The Cyber Express: What becomes harder to secure when applications behave dynamically?

Rahul Sood: AI-native applications introduce another layer because some risks only become visible through behaviour. The security question is no longer limited to whether the underlying code contains a known vulnerability. You also need to understand what the system is doing at runtime. One of the more concerning findings in the AI-native application security research we conducted last year was how basic the visibility problem still was. Many security practitioners told us they didn't have a reliable way of identifying all of the LLMs operating within their environment. That's a significant problem because you cannot meaningfully govern something you don't know exists. As applications become more dynamic, understanding behaviour becomes just as important as understanding what's in the code.

The Cyber Express: Is shift-left still enough?

Rahul Sood: Shift-left was an important correction because security was happening far too late. But moving security earlier in development doesn't solve the entire problem on its own, since risk doesn't only enter through code. It can come through a dependency, an API, a model interaction, a deployment configuration, or the way an application behaves once it's actually running. There is also a growing mismatch between how quickly vulnerabilities can be exploited and how long organisations take to remediate them. The 2026 Edgescan Vulnerability Statistics Report puts the average time to close high and critical vulnerabilities at roughly 55 days. At the other end of that equation, exploitation of newly disclosed vulnerabilities can now begin within hours. Finding a vulnerability earlier is useful, but the more consequential metric is the period between discovery and protection. The next phase of application security is less about moving a scanner to an earlier point in the lifecycle and more about compressing the entire exposure window, from discovery to protection.

The Cyber Express: How can teams identify which risks actually matter?

Rahul Sood: Security teams don't have an alert problem as much as they have a context problem. A vulnerability can have a severe technical rating and still present relatively little immediate risk if the affected component isn't reachable or exposed. Another vulnerability with a lower score could sit in an internet-facing, business-critical application and deserve immediate attention. So prioritisation has to move beyond severity. That means combining technical severity with signals like EPSS scores for real-world exploit probability, static reachability analysis to confirm whether a vulnerable path can actually be called, and false-positive reduction to cut out findings that never posed real risk in the first place. The goal shouldn't be to help a security team process 10,000 alerts faster. It should be to make sure they don't have to treat 10,000 alerts as equally important in the first place.

The Cyber Express: What does a healthy developer–security relationship look like?

Rahul Sood: The healthiest organisations I've seen don't treat security as a handoff between two teams. Developers understand that security is part of building good software, while security teams take responsibility for making the secure path practical. If following security policy means leaving the developer workflow, opening a ticket, waiting several days and then interpreting a scanner report, people will inevitably look for ways around the process. That's an operating-model problem, not simply a developer behaviour problem. We saw how wide that gap had become in research we conducted last year. Seventy-four percent of security professionals surveyed said developers viewed security as a blocker to AI innovation. Security teams should define acceptable risk, policies and guardrails. Engineering teams should then be able to work within those boundaries without constantly asking for permission. That is ultimately what a mature DevSecOps model should achieve. Security becomes part of the engineering system rather than another team standing beside it.

The Cyber Express: What one assumption should organisations rethink today?

Rahul Sood: One assumption I would rethink is that knowing what software contains is enough to understand its security posture. Application security has traditionally been very good at analysing components: the code, libraries, dependencies, APIs and vulnerabilities that make up an application. But with autonomous software, knowing what it's made of is only part of the picture. We also need to understand the authority we've given it. An agent may be built from approved components and have no obvious vulnerability in its code, yet still create significant risk because it can access the wrong data, has excessive permissions or can take an action that was never anticipated when those permissions were granted. We have spent decades building security around software that executes instructions written by people. We are now introducing software that can interpret a goal, decide how to pursue it and interact with other systems along the way. The security architecture for that world cannot rely only on finding vulnerabilities. It also has to govern delegated authority. I think that will become one of the defining application security problems of the agentic era.

Want to Be Part of Security Pill?

Are you a cybersecurity professional, researcher, CISO or industry expert with insights to share? The Cyber Express is always looking to bring new voices and perspectives to the cybersecurity community.

If you would like to be featured on Security Pill or discuss a potential podcast collaboration, reach out to us at [samiksha.jain@thecyberexpress.com].

Have a story, expertise, or perspective to share? Let’s start the conversation.
  • ✇Security | CIO
  • 16 governance tools for securing your AI fleet
    Every DevOps team member knows that dealing with an AI is like being a circus lion tamer. The boss and the audience are happy when the lions sit on the pedestal and roar on cue, but there’s always the danger that they’ll go rogue and bring the whole show to a quick and disastrous end. The reality is that running LLMs in production means being ready to handle hallucinations, data leakage, misinformation, madness, or worse. Lately, a range of companies have emerged to offe
     

16 governance tools for securing your AI fleet

18 de Setembro de 2026, 07:01

Every DevOps team member knows that dealing with an AI is like being a circus lion tamer. The boss and the audience are happy when the lions sit on the pedestal and roar on cue, but there’s always the danger that they’ll go rogue and bring the whole show to a quick and disastrous end.

The reality is that running LLMs in production means being ready to handle hallucinations, data leakage, misinformation, madness, or worse. Lately, a range of companies have emerged to offer tools, platforms, and systems aimed at helping AIOps teams shoulder the load of keeping AIs in check. They’re building algorithms that are an amalgamation of psychiatrist, police officer, social worker, politruk, and office snitch. Yes, some have their own LLMs buried inside, an irony not lost on anyone, but at least they’re trying.

These tools often have words like “guardrails,” “governance,” or “trust” in their name. They’re closely related to other AIOps tools that focus on development or benchmarking. These emerging product categories often overlap and that overlap will only grow as AI — and the market surrounding the technology — continues to evolve.

Here in alphabetical order are 16 of the most interesting companies working on controlling the wonderful and dangerous LLMs that promise to do so much — as long as they can be kept on that pedestal and behaving.

Collibra

Data products require controlling access enough to stop leaks while providing just the right data that the clients need. Collibra offers an AI Command Center that merges tools for governance and privacy protection with general agent management jobs such as minimizing the number of hallucinations. This Unified Control Plane catalogs the various agents and begins by giving each a “trust score” that measures its riskiness and its readiness. The tool integrates well with other data management tools such as Snowflake or Databricks so that managers can control access by drilling down into full audit trails.

Pricing: No standard pricing information is available. Schedule a demonstration and meet with the sales team.

Standout feature: A focus on securing data across the entire enterprise means that AI governance is just part of a larger strategic goal on accuracy.

Best suited for: Large enterprises that need consistent data quality.

Confident Security

Testing and tracking AI compliance often requires dealing with lots of private or sensitive information and that’s why Confident Security developed a tool for offering privacy to cloud computing called OpenPCC, a name that nods to Apple’s Private Cloud Compute. Teams can choose between the open-source core or the commercial product with a full-fledged SaaS platform and API. Both use layers of encryption to ensure AI inference is computed inside a secure and, if needed, anonymous system and released only when it passes a set of tests. The extra layers of privacy-enforcing math help for applications with more sensitive queries.

Pricing: Usage-based pricing metered by tokens.

Standout feature: Predefined compliance wrappers for specific industries allows them to say, “We take financial responsibility for any breach or misuse.”

Best suited for: Compliance-constrained industries such as healthcare or financial services.

Credo AI

Tracking agents and watching them for risky behavior is what Credo AI was built to do. Enterprise DevOps teams can build a centralized catalog of agents or LLMs and then refer to this “Govern AI Assistant” (GAIA) regularly to keep compliance issues under control. The tool works at all levels from the lowest model level through the agent level, application level, and network level, controlling data flows when necessary. Credo’s system offers “policy packs” for regulatory frameworks from around the world, including the EU AI Act, SOC2, ISO-42001, and the GDPR. Automated guardrails can either track compliance or actively enforce policies.

Pricing: Meet with the sales team for a quote.

Standout feature: Risk assessment aimed at international regulatory frameworks.

Best suited for: Large, multi-country enterprises.

F5 with CalypsoAI

Combining the new with the old is a good way to extend LLM security to a full enterprise. F5 acquired CalypsoAI to add automated red teaming and a portfolio of tools for defending against AI adversaries. CalypsoAI is designed to wrap around the LLMs and make defensive moves at the inference layer by tracking data flows. When necessary, it can probe the models for new or known potential weaknesses.

Pricing: Available from sales team.

Standout feature: Real-time tools and integrated automated red teaming watch for problems sicj as prompt abuse.

Best suited for: High-volume applications such as publicly facing web applications.

Fiddler AI

“All enterprise agents need the AI control plane,” announces the sales literature from Fiddler AI. Its product tracks the behavior of a model and attempts to offer a solid set of explanations for what it does. This knowledge can become the basis for governing and securing the model, as well as assuring its compliance with various rules and regulations. Fiddler works at enterprise scale to aggregate unlimited copies of more than 100-plus metrics to track hallucinations, toxicity, personally identifiable information (PII) leakage, and drift. Then it enforces policies to protect against violations such as jailbreaks, prompt injections, or bias. The goal is to handle the immense scale of telemetry necessary for keeping large deployments of models focused on delivering what the enterprise needs.

Pricing: Free tier for developers; metered usage based on counting traces.

Standout feature: Unified platform integrates guardrails with general model maintenance.

Best suited for: Teams running stacks full of predictive ML models.

Guardrails AI  

Guardrails AI offers two main products: Snowglobe, a simulator that feeds endless streams of synthetic data to your model and looks for anomalies; and Guardrails OSS, a production-ready framework that watches over your LLM and fixes issues in the output. Guardrails OSS enforces various rules or “validators” that address a wide range of issues, including blocking bad responses (PII leakage, jailbreaking) and fixing minor issues such as misformatted JSON. It’s designed to wrap around any LLM and provide a set of, well, guardrails around all interactions.

Pricing: Open-source tools can also be managed.

Standout feature: Real-time surveillance of model behavior for securing outward-facing tools.

Best suited for: Structured applications that need LLM responses to conform to set formats.

Hidden Layer

Finding the deepest weaknesses can be difficult with any computer system, but the challenge is even greater with the inscrutable nature of LLMs. Hidden Layer’s platform offers a comprehensive defense that begins with a cataloging approach intent on finding even the most difficult to locate AIs deep in the stack. From there, it deploys attack simulators and automated red teams to continue to probe the models it identifies. Its AI Bill of Materials (AI BoM) digs into the models themselves, searching through the weights and metadata for weaknesses. The multilayered approach aims to ensure all the models across the enterprise stack are behaving correctly and not leaking crucial information.

Pricing: Available from cloud marketplaces such as AWS, with custom pricing for installations.

Standout feature: AI Attack Simulation enables developers to anticipate potential problems.

Best suited for: Cyberteams securing proprietary models.

IBM’s watsonx.governance

Controlling a constellation of agents and models with IBM’s watsonx.governance begins with building a governance graph that tracks the risk associated with all deployed models. The system tracks development and deployment so that stakeholders can get fast answers on what data went into training and what answers were coming out of the model. DevOps teams can track standard metrics that arrive out-of-the-box, such as model drift or hallucination percentages, or they can define their own. The approach often builds off synergies with other IBM products running in the IBM Cloud.

Pricing: After a free 30-day trial, costs are metered according to usage and deployment breadth.

Standout feature: Full lifecycle integration aimed at covering all governance issues across large clouds.

Best suited for: Large enterprises with multicloud deployments in compliance-heavy environments.

Lakera

When end-users interact directly with the LLM by speaking or writing questions and prompts, Lakera is there to watch over the traffic and keep the agents in line. The tool is designed to stop the kind of jailbreaking and prompt injection games that allow end-users to attack systems. Granular access controls enable fine-grained orchestration of the interactions to prevent unauthorized data leakage or other attacks. The company also sponsors “Gandalf,” a well-known educational game that teaches AI security, and many feel that their tools are better because of the knowledge gained from developing and running it.

Pricing: Free community tier for Lakera Guard with limited requests and tokens. Larger limits come from a meeting with the sales team.

Standout feature: High-speed tool optimized for low-latency protection

Best suited for: Teams delivering answers in real-time environments.

Lasso Security

Taking control of AI across a large enterprise platform requires a breadth of knowledge and the tools to control it. Lasso Security begins with a comprehensive census of AI implementations driven by automated tools that can find the various LLMs through techniques such as scanning source code repositories and continuous integration pipelines. This inventory becomes the foundation for red teaming and runtime enforcement.

Pricing: Custom pricing for each deployment.

Standout feature: Agentic tools aimed at multi-step workflows.

Best suited for: Teams securing relatively open environments that encourage experimentation.

LogicGate

CISOs in need of a no-code approach to governance, risk, and compliance can turn to the Risk Cloud from LogicGate. The tool fills a graph database with information and an assessment of all the services in the enterprise. One aspect tracks potential problems exacerbated by LLMs. Its tool reaches out through prebuilt connections to create what the company calls a “Risk Cloud Quantify,” a value that estimates the potential danger using a mixture of data collection and Monte Carlo simulations. LogicGate also tackles more general problems of management such as evaluating business tradeoffs with its Value Realization Tool. Together, this gives leadership the centralized reporting system for making data-informed decisions.

Pricing: Pricing available after a demonstration.

Standout feature: Deep integration with governance models for traditional stacks.

Best suited for: Full-stack implementations that need governance control over AI and traditional code.

Mindgard

The AI attack surface is broad, varied, and largely unknown. Mindgard’s platform is designed to be an “automated red team,” a set of programs and AIs that constantly poke and try to disrupt the target. Through a constant cycle of discovery, recon, attacking, and defense re-evaluation, the system searches for risks and proposes guardrail improvements to eliminate them. Designed to work with the major LLMs and agents, the system can be deployed quickly to deliver a fast security assessment and — often — a set of new holes to plug.

Pricing: Sandboxed free tier and interactive pricing for larger workflows.

Standout feature: Automated red-teaming simulator deploys hundreds of potential threats for testing.

Best suited for: Security researchers maintaining a high tempo of vigilance.

OneTrust

Managing personal information and other legal restrictions are increasingly important for anyone developing an AI solution. Once you start mixing private data into the training corpus, the DevOps team needs a plan. OneTrust offers what the company calls “Continuous Governance,” which largely means a platform that mixes cataloging, monitoring, and filtering for an entire enterprise stack. AI is just part of a larger system that also manages databases and other compliance challenges. The goal is to offer the right legal filters so that AI developers can get back to wrestling with prompts and context windows.

Pricing: Pricing available by request.

Standout feature: Global privacy compliance database aimed at tracking worldwide compliance issues.

Best suited for: Large, regulated multinational companies.

Prompt Security

When the right solution is to put one gatekeeper for access to an LLM, Prompt Security offers a proxy gateway that will filter the data flowing in and out, watching for any kind of malfeasance. The approach is said to be “LLM agnostic,” which means that it focuses on scrutinizing the input and output, not the internal state of the agents. Prompt Security promises deep content inspection of these data flows, something essential for detecting leaks of PII or other protected information. This architecture also makes it easier to integrate the tool with a variety of AI-driven applications, both internal and external.

Pricing: Open-source tools for developers; full support for deployment available from the sales team.

Standout feature: AI Security Academy for building a depth of knowledge.

Best suited for: Teams able to leverage the power of open source.

Protect AI

Sometimes dangers lurk deep inside a stack, often in hidden locations. The collection of tools from Protect AI examines the whole pipeline for flaws, weaknesses, or hidden problems. The most outward-facing tool, Guardian, is a collection of scanners that use a configurable set of rules to dig deep into the data flows as part of your CI/CD pipeline. Its partner, Recon, offers an automated red team with a predefined collection of attacks to be tested immediately. A third part, Layer, integrates AI security with general cybersecurity tools to build a comprehensive defense. Finally, two open-source project — LLM Security and ModelScan — offer localized, model-agnostic answers.

Pricing: Custom pricing based on the number of models, pipelines, and agents

Standout feature: End-to-end integration for protecting the entire data pipeline.

Best suited for: Teams defending complex pipelines and workflows.

Securiti.ai

One of the top goals for anyone working with AI is to make sure that the same rules of sharing data from, say, a database also apply to all the LLMs running on the same platforms. Security.ai calls this process data security posture management (DSPM). Its centralized platform searches out, identifies, and tracks all the agents running inside the enterprise and then controls their behavior with tools such as Context-Sensitive Firewalls, Data Minimization, and Data Flow Governance. Several dozen tools work together in a constellation that harmonizes governance with privacy awareness and data management.

Pricing: Custom pricing based on volume.

Standout feature: Data command Center for linking all security issues.

Best suited for: Enterprises with complex workflows with custom data governance issues.

  • ✇Security | CIO
  • OpenAI admits six new misalignment incidents under new reporting framework
    OpenAI has published six new reports detailing AI model misalignment, including instances of hidden instructions, unauthorized communication, and attempts to locate exposed API keys, adding to the evidence that its AI systems bypassed controls during testing. The reports, based on internal evaluations, describe models taking actions beyond defined constraints, including modifying intermediate outputs, interacting with external services, and using shared environments in
     

OpenAI admits six new misalignment incidents under new reporting framework

17 de Setembro de 2026, 12:51

OpenAI has published six new reports detailing AI model misalignment, including instances of hidden instructions, unauthorized communication, and attempts to locate exposed API keys, adding to the evidence that its AI systems bypassed controls during testing.

The reports, based on internal evaluations, describe models taking actions beyond defined constraints, including modifying intermediate outputs, interacting with external services, and using shared environments in unintended ways, according to the company.

OpenAI termed the model’s behaviour as “unexpected or concerning“.

The cases show how models behave when given access to tools, memory, and external systems, conditions that increasingly mirror enterprise deployments.

The disclosures come alongside a new reporting framework introduced by OpenAI to track and publish such incidents, based on internal evaluations of model behavior.

Prompt injection and workflow manipulation

Two of the incidents center on how models handled “compaction summaries” or condensed versions of their prior context used to perform long tasks without exceeding the models’ attention span. In these cases, the models inserted their own instructions into those summaries. OpenAI said one model “added unauthorized instructions to its compaction summaries,” allowing those instructions to influence subsequent steps.

“We observed rare cases of a model writing jailbreak-like instructions into its own compaction,” OpenAI wrote in one report detailing the model’s behavior. “Our conclusion was that this behavior was extremely rare, did not confer an obvious reward advantage, and was monitorable.”

In another case, the summaries were used to encourage deceptive behavior in later outputs, creating a mechanism through which instructions introduced at one stage could persist across interactions.

External communication and boundary bypass

Other reports describe models bypassing communication constraints by using external internet services. In one case, a model used “temporary file hosting services” to exchange information outside intended channels, according to OpenAI. In another, a model uploaded content to the internet so it could later cite that material in its response, effectively creating a reference source it could use to complete a task.

A separate incident involved a model attempting to locate sensitive information by searching public repositories. OpenAI said the model engaged in “searching GitHub for leaked API keys,” demonstrating how systems can identify exposed credentials.

Another case described unauthorized writes to an internal artifact repository, which were then used to enable communication across otherwise isolated evaluation samples, creating a mechanism for cross-sample interaction.

From model behavior to enterprise risk

While the behaviors were observed in controlled testing environments, analysts said the patterns are relevant as organizations deploy AI systems with access to business workflows and data.

“These behaviors are not training-specific traits,” said Yih Khai Wong, senior research manager at IDC. “The failure classes they reveal… are portable to production environments,” particularly as agents are optimized to complete tasks even when constrained.

The implication, analysts said, is that the focus shifts from individual model outputs to how those behaviors translate into system-level actions.

“The risk becomes material when an AI agent has access to corporate data, credentials, external services or business workflows,” said Apeksha Kaushik, senior principal analyst at Gartner, adding that organizations should assume safeguards can fail and design controls accordingly.

Vibhum Dubey, a cybersecurity researcher and red teamer, said the shift comes when models are embedded into operational systems. “An agent that can read an email, inspect a repository, access a cloud environment… becomes part of the enterprise attack surface,” he said, pointing to how multiple permitted actions can be chained together.

The disclosures also highlight how models interact with memory and reusable context in ways that can influence future behavior. Analysts said this introduces risks such as persistent, unauthorized changes to an agent’s behavior across sessions, particularly when context is reused without validation.

Kaushik said organizations should focus on how systems are designed around the model, not just the model itself. The key question, she said, is whether the surrounding architecture can “prevent, detect and contain an unsafe action.”

Framework formalizes disclosures

Across the six reports, OpenAI said the cases “describe individual instances” and are not intended to represent how frequently such behavior occurs across its systems.

According to the company, the disclosures are being published under a new framework introduced by OpenAI to track and report model misalignment. The framework allows employees to flag unexpected or unauthorized behavior, which is then assessed to determine whether it meets the threshold for public disclosure.

“We do not believe that the AI industry has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer,” it said in the blog post. “This new framework is intended to expedite publishing misalignment reports following observation, even when we haven’t fully explained or mitigated the behavior we’re reporting.

This article first appeared on CSO.

  • ✇Security | CIO
  • Oracle’s September patches put Fusion Middleware back in the hot seat
    Oracle’s September 2026 Critical Security Patch Update has arrived with 673 new security patches spanning 17 Oracle product families, with Oracle E-Business Suite accounting for the largest share at 159 patches, followed by Fusion Middleware with 153. Of these, 19 E-Business Suite vulnerabilities and 78 Fusion Middleware vulnerabilities can be remotely exploited without authentication. Other product categories with 50 or more issues fixed in the rollout include Oracle D
     

Oracle’s September patches put Fusion Middleware back in the hot seat

16 de Setembro de 2026, 12:52

Oracle’s September 2026 Critical Security Patch Update has arrived with 673 new security patches spanning 17 Oracle product families, with Oracle E-Business Suite accounting for the largest share at 159 patches, followed by Fusion Middleware with 153. Of these, 19 E-Business Suite vulnerabilities and 78 Fusion Middleware vulnerabilities can be remotely exploited without authentication.

Other product categories with 50 or more issues fixed in the rollout include Oracle Database Server, Oracle Communications, and Oracle Analytics.

Oracle recently accelerated its patching rhythm from quarterly to monthly. It advised customers to apply the September patches immediately, warning that it continues to receive reports of successful attacks on its software where customers had not applied available fixes.

Five max-severity flaws sit in Fusion Middleware

The September update addresses five critical vulnerabilities carrying the maximum CVSS score of 10.0 within Fusion Middleware.

They affect Oracle Access Manager (CVE-2026-71133), Oracle Forms (CVE-2026-83099), Oracle Internet Directory (CVE-2026-83059), Oracle Platform Security for Java (CVE-2026-83020), and Oracle WebLogic Server (CVE-2026-83021).

All five are remotely exploitable without authentication over the network; attacking them is of low complexity and requires neither privileges nor user interaction.

The update also addressed a sixth CVSS 10.0 vulnerability, this one in Oracle Hyperion Financial Management (CVE-2026-87230); it too can be remotely exploited without authentication.

The update also includes 13 Fusion Middleware bugs with a CVSS score of 9.9, just below the maximum severity. These include CVE-2026-71163 and CVE-2026-73945 in Oracle Access Manager, CVE-2026-83055, CVE-2026-83057 and CVE-2026-83056 in Oracle Internet Directory, CVE-2026-83058, CVE-2026-73948 and CVE-2026-83039 in Oracle WebCenter Portal, CVE-2026-82999, CVE-2026-82997 and CVE-2026-82998 in Service Delivery Platform, and one each in Oracle WebCenter Sites (CVE-2026-83031) and Oracle WebLogic Server (CVE-2026-83038).

None of these are remotely exploitable without authentication. However, they require low privileges, remain network-accessible and can have high confidentiality and integrity impacts.

Oracle did not mark any of the six CVSS 10.0 and 13 CVSS 9.9 vulnerabilities as exploited in the wild.

Fusion Middleware has featured heavily in Oracle’s recent patch cycles too. Its July update addressed 10 CVSS 10.0 vulnerabilities, highlighting the product family’s recurring exposure to maximum-severity flaws.

Oracle’s patching message is as important as the patches

Until patches can be deployed, Oracle said, customers may reduce exposure by blocking network protocols required for an attack or removing unnecessary privileges and package access. However, it cautioned, these measures can break application functionality and should be tested on non-production systems. They are not to be considered long-term solutions because they do not address the underlying vulnerabilities, the company said in its September critical patch update advisory.

It also included a warning for organizations running older Oracle releases. The fixes are provided only for supported versions, the company said, adding that “Product releases that are not under Premier Support or Extended Support are not tested for the presence of vulnerabilities addressed by this Critical Security Patch Update.”

Also, for organizations that have skipped earlier security updates, Oracle advises reviewing previous CSPUs and quarterly Critical Patch Updates rather than assuming the September releases covers the backlog.

  • ✇Security | CIO
  • Salesforce, ServiceNow data targeted in ‘City-Forum’ attacks
    Records held in Salesforce and ServiceNow systems are under attack leaving user data exposed, according to researchers at Reco. The attack appears similar to those perpetrated by the extortion group ShinyHunters, Reco said. ShinyHunters has been particularly active this year, attacking dating sites in January and Oracle in June, and there are fears that they could have found a new target. Reco has named the latest campaign of attacks “City-Forum,” after a domain name
     

Salesforce, ServiceNow data targeted in ‘City-Forum’ attacks

14 de Agosto de 2026, 10:36

Records held in Salesforce and ServiceNow systems are under attack leaving user data exposed, according to researchers at Reco.

The attack appears similar to those perpetrated by the extortion group ShinyHunters, Reco said. ShinyHunters has been particularly active this year, attacking dating sites in January and Oracle in June, and there are fears that they could have found a new target.

Reco has named the latest campaign of attacks “City-Forum,” after a domain name associated with the attackers’ IP address. While it bears similarities to Shiny Hunters’ past exploits, there are also differences. This time around the attacker penetrated the systems through the UI-API layer, an attack point that Reco had not seen used before, and had also created its own toolset to carry out the attack. It is also targeting a native ServiceNow Service Portal search endpoint that has almost no online documentation or well-known open-source tools.

The threat is particularly noteworthy, Reco said, as the attackers have studied the services to map different common data-leak vectors, a sign of an advanced approach.

A Salesforce spokesperson said that it was aware of the campaign in which malicious actors are exploiting customers’ overly permissive Experience Cloud guest user configurations in the campaign to potentially access more data than targeted organizations intended.

“This issue highlights risks stemming from misconfigurations, such as overly permissive guest user profiles, and not from a Salesforce vulnerability,” the spokesperson said.

Regardless of who the attackers were and how the attack was carried out, one thing should be clear: Organizations should be increasingly careful about who they give login credentials to.

This article first appeared on CSO.

  • ✇The Cloudflare Blog
  • AI Security for Apps is now generally available Liam Reese · Zhiyuan Zheng · Catherine Newcomb
    Cloudflare’s AI Security for Apps detects and mitigates threats to AI-powered applications. Today, we're announcing that it is generally available.We’re shipping with new capabilities like detection for custom topics, and we're making AI endpoint discovery free for every Cloudflare customer—including those on Free, Pro, and Business plans—to give everyone visibility into where AI is deployed across their Internet-facing apps.We're also announcing an expanded collaboration with IBM, which has cho
     

AI Security for Apps is now generally available

11 de Março de 2026, 10:00

Cloudflare’s AI Security for Apps detects and mitigates threats to AI-powered applications. Today, we're announcing that it is generally available.

We’re shipping with new capabilities like detection for custom topics, and we're making AI endpoint discovery free for every Cloudflare customer—including those on Free, Pro, and Business plans—to give everyone visibility into where AI is deployed across their Internet-facing apps.

We're also announcing an expanded collaboration with IBM, which has chosen Cloudflare to deliver AI security to its cloud customers. And we’re partnering with Wiz to give mutual customers a unified view of their AI security posture.

A new kind of attack surface

Traditional web applications have defined operations: check a bank balance, make a transfer. You can write deterministic rules to secure those interactions. 

AI-powered applications and agents are different. They accept natural language and generate unpredictable responses. There's no fixed set of operations to allow or deny, because the inputs and outputs are probabilistic. Attackers can manipulate large language models to take unauthorized actions or leak sensitive data. Prompt injection, sensitive information disclosure, and unbounded consumption are just a few of the risks cataloged in the OWASP Top 10 for LLM Applications.

These risks escalate as AI applications become agents. When an AI gains access to tool calls—processing refunds, modifying accounts, providing discounts, or accessing customer data—a single malicious prompt becomes an immediate security incident.

Customers tell us what they’re up against. "Most of Newfold Digital's teams are putting in their own Generative AI safeguards, but everybody is innovating so quickly that there are inevitably going to be some gaps eventually,” says Rick Radinger, Principal Systems Architect at Newfold Digital, which operates Bluehost, HostGator, and Domain.com.

What AI Security for Apps does

We built AI Security for Apps to address this. It sits in front of your AI-powered applications, whether you're using a third-party model or hosting your own, as part of Cloudflare's reverse proxy. It helps you (1) discover AI-powered apps across your web property, (2) detect malicious or off-policy behavior to those endpoints, and (3) mitigate threats via the familiar WAF rule builder.

Discovery — now free for everyone

Before you can protect your LLM-powered applications, you need to know where they're being used. We often hear from security teams who don’t have a complete picture of AI deployments across their apps, especially as the LLM market evolves and developers swap out models and providers. 

AI Security for Apps automatically identifies LLM-powered endpoints across your web properties, regardless of where they’re hosted or what the model is. Starting today, this capability is free for every Cloudflare customer, including Free, Pro, and Business plans. 

Cloudflare’s dashboard page of web assets, showing 2 example endpoints labelled as cf-llm

Discovering these endpoints automatically requires more than matching common path patterns like /chat/completions. Many AI-powered applications don't have a chat interface: think product search, property valuation tools, or recommendation engines. We built a detection system that looks at how endpoints behave, not what they're called. To confidently identify AI-powered endpoints, sufficient valid traffic is required.

AI-powered endpoints that have been discovered will be visible under Security → Web Assets, labeled as cf-llm. For customers on a Free plan, endpoint discovery is initiated when you first navigate to the Discovery page. For customers on a paid plan, discovery occurs automatically in the background on a recurring basis. If your AI-powered endpoints have been discovered, you can review them immediately.

Detection

AI Security for Apps detections follow the always-on approach for traffic to your AI-powered endpoints. Each prompt is run through multiple detection modules for prompt injection, PII exposure, and sensitive or toxic topics. The results—whether the prompt was malicious or not—are attached as metadata you can use in custom WAF rules to enforce your policies. We are continuously exploring ways to leverage our global network, which sees traffic from roughly 20% of the web, to identify new attack patterns across millions of sites before they reach yours.

New in GA: Custom topics detection

The product ships with built-in detection for common threats: prompt injections, PII extraction, and toxic topics. But every business has its own definition of what's off-limits. A financial services company might need to detect discussions of specific securities. A healthcare company might need to flag conversations that touch on patient data. A retailer might want to know when customers are asking about competitor products.

The new custom topics feature lets you define these categories. You specify the topic, we inspect the prompt and output a relevance score that you can use to log, block, or handle however you decide. Our goal is to build an extensible tool that flexes to your use cases.

Prompt relevance score inside of AI Security for Apps

New in GA: Custom prompt extraction

AI Security for Apps enforces guardrails before unsafe prompts can reach your infrastructure. To run detections accurately and provide real-time protection, we first need to identify the prompt within the request payload. Prompts can live anywhere in a request body, and different LLM providers structure their APIs differently. OpenAI and most providers use $.messages[*].content for chat completions. Anthropic's batch API nests prompts inside $.requests[*].params.messages[*].content. Your custom property valuation tool might use $.property_description.

Out of the box, we support the standard formats used by OpenAI, Anthropic, Google Gemini, Mistral, Cohere, xAI, DeepSeek, and others. When we can't match a known pattern, we apply a default-secure posture and run detection on the entire request body. This can introduce false positives when the payload contains fields that are sensitive but don't feed directly to an AI model, for example, a $.customer_name field alongside the actual prompt might trigger PII detection unnecessarily.

Soon, you'll be able to define your own JSONPath expressions to tell us exactly where to find the prompt. This will reduce false positives and lead to more accurate detections. We're also building a prompt-learning capability that will automatically adapt to your application's structure over time.

Mitigation

Once a threat is identified and scored, you can block it, log it, or deliver custom responses, using the same WAF rules engine you already use for the rest of your application security. The power of Cloudflare’s shared platform is that you can combine AI-specific signals with everything else we know about a request, represented by hundreds of fields available in the WAF. A prompt injection attempt is suspicious. A prompt injection attempt from an IP that’s been probing your login page, using a browser fingerprint associated with previous attacks, and rotating through a botnet is a different story. Point solutions that only see the AI layer can’t make these connections.

This unified security layer is exactly what they need at Newfold Digital to discover, label, and protect AI endpoints, says Radinger: “We look forward to using it across all these projects to serve as a fail-safe."

Growing ecosystem

AI Security for Applications will also be available through Cloudflare's growing ecosystem, including through integration with IBM Cloud. Through IBM Cloud Internet Services (CIS), end users can already procure advanced application security solutions and manage them directly through their IBM Cloud account. 

We're also partnering with Wiz to connect AI Security for Applications with Wiz AI Security, giving mutual customers a unified view of their AI security posture, from model and agent discovery in the cloud to application-layer guardrails at the edge.

How to get started

AI Security for Apps is available now for Cloudflare’s Enterprise customers. Contact your account team to get started, or see the product in action with a self-guided tour.

If you're on a Free, Pro, or Business plan, you can use AI endpoint discovery today. Log in to your dashboard and navigate to Security → Web Assets to see which endpoints we've identified. Keep an eye out — we plan to make all AI Security for Apps capabilities available for customers on all plans soon.

For configuration details, see our documentation.

Benchmarking AI Pentesting Tools: A Practical Comparison

30 de Abril de 2026, 08:31

We benchmarked 4 AI pentesting tools: Escape, Shannon, Strix, PentAGI, and Claude against a modern vulnerable application. Learn more about their detection rates, false positive rates, and scanning speed.

The post Benchmarking AI Pentesting Tools: A Practical Comparison appeared first on Security Boulevard.

  • ✇Security Boulevard
  • Microsoft’s April Security Update of High-Risk Vulnerability Notice for Multiple Products NSFOCUS
    Overview On April 15, NSFOCUS CERT detected that Microsoft released the April Security Update patch, fixing 165 security issues involving Windows, Microsoft Office, Microsoft SQL Server, Microsoft Visual Studio, Microsoft .NET Framework, Widely used products such as Azure, including high-risk vulnerability types such as privilege escalation and remote code execution. Among the vulnerabilities fixed by […] The post Microsoft’s April Security Update of High-Risk Vulnerability Notice for Multiple P
     

Microsoft’s April Security Update of High-Risk Vulnerability Notice for Multiple Products

24 de Abril de 2026, 05:33

Overview On April 15, NSFOCUS CERT detected that Microsoft released the April Security Update patch, fixing 165 security issues involving Windows, Microsoft Office, Microsoft SQL Server, Microsoft Visual Studio, Microsoft .NET Framework, Widely used products such as Azure, including high-risk vulnerability types such as privilege escalation and remote code execution. Among the vulnerabilities fixed by […]

The post Microsoft’s April Security Update of High-Risk Vulnerability Notice for Multiple Products appeared first on NSFOCUS.

The post Microsoft’s April Security Update of High-Risk Vulnerability Notice for Multiple Products appeared first on Security Boulevard.

Julius v0.2.0: From 33 to 63 Probes — Now Detecting Cloud AI, Enterprise Inference, and RAG Pipelines

24 de Março de 2026, 22:13

TL;DR: Julius v0.2.0 nearly doubles LLM fingerprinting probe coverage from 33 to 63, adding detection for cloud-managed AI services (AWS Bedrock, Azure OpenAI, Vertex AI), high-performance inference servers (SGLang, TensorRT-LLM, Triton), AI gateways (Portkey, Helicone, Bifrost), and self-hosted RAG platforms (PrivateGPT, RAGFlow, Quivr). This release also hardens the scanner itself with response size limiting and […]

The post Julius v0.2.0: From 33 to 63 Probes — Now Detecting Cloud AI, Enterprise Inference, and RAG Pipelines appeared first on Praetorian.

The post Julius v0.2.0: From 33 to 63 Probes — Now Detecting Cloud AI, Enterprise Inference, and RAG Pipelines appeared first on Security Boulevard.

  • ✇Security Boulevard
  • CVE-2026-20963: SharePoint Deserialization Remote Code Execution Vulnerability Deepak Kumar Choudhary
    Microsoft SharePoint, a core platform for enterprise collaboration, is facing active exploitation through a newly confirmed vulnerability, tracked as CVE-2026-20963. Rooted in unsafe deserialization of user-controlled data, this vulnerability allows remote. The post CVE-2026-20963: SharePoint Deserialization Remote Code Execution Vulnerability appeared first on Indusface. The post CVE-2026-20963: SharePoint Deserialization Remote Code Execution Vulnerability appeared first on Security Boulevard.
     

CVE-2026-20963: SharePoint Deserialization Remote Code Execution Vulnerability

24 de Março de 2026, 04:30

Microsoft SharePoint, a core platform for enterprise collaboration, is facing active exploitation through a newly confirmed vulnerability, tracked as CVE-2026-20963. Rooted in unsafe deserialization of user-controlled data, this vulnerability allows remote.

The post CVE-2026-20963: SharePoint Deserialization Remote Code Execution Vulnerability appeared first on Indusface.

The post CVE-2026-20963: SharePoint Deserialization Remote Code Execution Vulnerability appeared first on Security Boulevard.

  • ✇Security Boulevard
  • The Real State of Offensive Security: AI, Penetration Testing & The Road Ahead with Andrew Wilson Tom Eston
    Tom Eston interviews offensive AI researcher and PhD candidate Andrew Wilson, a former Bishop Fox partner who helped grow the firm from under 20 people to nearly 500, built award-winning AI solutions for SOC modernization, founded Cactus Con, and relocated his family to Guadalajara to open and scale a Bishop Fox office. They discuss Mexico’s […] The post The Real State of Offensive Security: AI, Penetration Testing & The Road Ahead with Andrew Wilson appeared first on Shared Security Podcast
     
  • ✇Security Boulevard
  • RSAC 2026 Innovation Sandbox | ZeroPath: From Alarm Accumulation to Executable Fixes NSFOCUS
    Company Profile ZeroPath is an AI-native application security startup founded in 2024, and its core products also use the eponymous brand ZeroPath. The company focuses on using AI to automatically discover, verify and fix code vulnerabilities, trying to break through the limitations of traditional SAST, SCA, Secrets scanning and IaC scanning that are fighting each […] The post RSAC 2026 Innovation Sandbox | ZeroPath: From Alarm Accumulation to Executable Fixes appeared first on NSFOCUS, Inc., a
     

RSAC 2026 Innovation Sandbox | ZeroPath: From Alarm Accumulation to Executable Fixes

22 de Março de 2026, 00:05

Company Profile ZeroPath is an AI-native application security startup founded in 2024, and its core products also use the eponymous brand ZeroPath. The company focuses on using AI to automatically discover, verify and fix code vulnerabilities, trying to break through the limitations of traditional SAST, SCA, Secrets scanning and IaC scanning that are fighting each […]

The post RSAC 2026 Innovation Sandbox | ZeroPath: From Alarm Accumulation to Executable Fixes appeared first on NSFOCUS, Inc., a global network and cyber security leader, protects enterprises and carriers from advanced cyber attacks..

The post RSAC 2026 Innovation Sandbox | ZeroPath: From Alarm Accumulation to Executable Fixes appeared first on Security Boulevard.

  • ✇Security Boulevard
  • CanisterWorm: The Self-Spreading npm Attack That Uses a Decentralized Server to Stay Alive Tom Abai
    On March 20, 2026 at 20:45 UTC, Aikido Security detected an unusual pattern across the npm registry: dozens of packages from multiple organizations were receiving unauthorized patch updates, all containing the same hidden malicious code. What they had caught was CanisterWorm, a self-spreading npm worm deployed by the threat actor group TeamPCP. We track this […] The post CanisterWorm: The Self-Spreading npm Attack That Uses a Decentralized Server to Stay Alive appeared first on Security Boulevar
     

CanisterWorm: The Self-Spreading npm Attack That Uses a Decentralized Server to Stay Alive

21 de Março de 2026, 17:37

On March 20, 2026 at 20:45 UTC, Aikido Security detected an unusual pattern across the npm registry: dozens of packages from multiple organizations were receiving unauthorized patch updates, all containing the same hidden malicious code. What they had caught was CanisterWorm, a self-spreading npm worm deployed by the threat actor group TeamPCP. We track this […]

The post CanisterWorm: The Self-Spreading npm Attack That Uses a Decentralized Server to Stay Alive appeared first on Security Boulevard.

❌
❌