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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 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.

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 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.

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 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.

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 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.

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 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.

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 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.

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