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What Anthropic’s Mythos Means for the Future of Cybersecurity

Two weeks ago, Anthropic announced that its new model, Claude Mythos Preview, can autonomously find and weaponize software vulnerabilities, turning them into working exploits without expert guidance. These were vulnerabilities in key software like operating systems and internet infrastructure that thousands of software developers working on those systems failed to find. This capability will have major security implications, compromising the devices and services we use every day. As a result, Anthropic is not releasing the model to the general public, but instead to a ...

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Mythos and Cybersecurity

Last week, Anthropic pulled back the curtain on Claude Mythos Preview, an AI model so capable at finding and exploiting software vulnerabilities that the company decided it was too dangerous to release to the public. Instead, access has been restricted to roughly 50 organizations—Microsoft, Apple, Amazon Web Services, CrowdStrike and other vendors of critical infrastructure—under an initiative called Project Glasswing.

The announcement was accompanied by a barrage of hair-raising anecdotes: thousands of vulnerabilities uncovered across every major...

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AI Infrastructure LiteLLM Supply Chain Poisoning Alert

Overview Recently, NSFOCUS Technology CERT detected that the GitHub community disclosed that there was a credential stealing program in the new version of LiteLLM. Analysis confirmed that it had suffered supply chain poisoning by the TeamPCP group on PyPI. It stole the publishing permission credentials by hacking into the security scanning tool Trivy used in […]

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As the US Midterms Approach, AI Is Going to Emerge as a Key Issue Concerning Voters

In December, the Trump administration signed an executive order that neutered states’ ability to regulate AI by ordering his administration to both sue and withhold funds from states that try to do so. This action pointedly supported industry lobbyists keen to avoid any constraints and consequences on their deployment of AI, while undermining the efforts of consumers, advocates, and industry associations concerned about AI’s harms who have spent years pushing for state regulation.

Trump’s actions have clarified the ideological alignments around AI within America’s electoral factions. They set down lines on a new playing field for the midterm elections, prompting members of his party, the opposition, and all of us to consider where we stand in the debate over how and where to let AI transform our lives...

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Team Mirai and Democracy

Japan’s election last month and the rise of the country’s newest and most innovative political party, Team Mirai, illustrates the viability of a different way to do politics.

In this model, technology is used to make democratic processes stronger, instead of undermining them. It is harnessed to root out corruption, instead of serving as a cash cow for campaign donations.

Imagine an election where every voter has the opportunity to opine directly to politicians on precisely the issues they care about. They’re not expected to spend hours becoming policy experts. Instead, an ...

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

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.

Academia and the “AI Brain Drain”

In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.

Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see ...

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Canada Needs Nationalized, Public AI

Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?

Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon...

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LLMs Generate Predictable Passwords

LLMs are bad at generating passwords:

There are strong noticeable patterns among these 50 passwords that can be seen easily:

  • All of the passwords start with a letter, usually uppercase G, almost always followed by the digit 7.
  • Character choices are highly uneven ­ for example, L , 9, m, 2, $ and # appeared in all 50 passwords, but 5 and @ only appeared in one password each, and most of the letters in the alphabet never appeared at all.
  • There are no repeating characters within any password. Probabilistically, this would be very unlikely if the passwords were truly random ­ but Claude preferred to avoid repeating characters, possibly because it “looks like it’s less random”.
    ...

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Insights into Claude Code Security: A New Pattern of Intelligent Attack and Defense

On February 20, 2026, AI company Anthropic released a new code security tool called Claude Code Security. This release coincided with the highly sensitive period of global capital markets to AI technology subverting the traditional software industry, which quickly triggered violent fluctuations in the capital market and caused the fall of stock prices of major […]

The post Insights into Claude Code Security: A New Pattern of Intelligent Attack and Defense appeared first on NSFOCUS, Inc., a global network and cyber security leader, protects enterprises and carriers from advanced cyber attacks..

The post Insights into Claude Code Security: A New Pattern of Intelligent Attack and Defense appeared first on Security Boulevard.

Is AI Good for Democracy?

Politicians fixate on the global race for technological supremacy between US and China. They debate geopolitical implications of chip exports, latest model releases from each country, and military applications of AI. Someday, they believe, we might see advancements in AI tip the scales in a superpower conflict.

But the most important arms race of the 21st century is already happening elsewhere and, while AI is definitely the weapon of choice, combatants are distributed across dozens of domains.

Academic journals are flooded with AI-generated papers, and are turning to AI to help review submissions. Brazil’s ...

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Side-Channel Attacks Against LLMs

Here are three papers describing different side-channel attacks against LLMs.

Remote Timing Attacks on Efficient Language Model Inference“:

Abstract: Scaling up language models has significantly increased their capabilities. But larger models are slower models, and so there is now an extensive body of work (e.g., speculative sampling or parallel decoding) that improves the (average case) efficiency of language model generation. But these techniques introduce data-dependent timing characteristics. We show it is possible to exploit these timing differences to mount a timing attack. By monitoring the (encrypted) network traffic between a victim user and a remote language model, we can learn information about the content of messages by noting when responses are faster or slower. With complete black-box access, on open source systems we show how it is possible to learn the topic of a user’s conversation (e.g., medical advice vs. coding assistance) with 90%+ precision, and on production systems like OpenAI’s ChatGPT and Anthropic’s Claude we can distinguish between specific messages or infer the user’s language. We further show that an active adversary can leverage a boosting attack to recover PII placed in messages (e.g., phone numbers or credit card numbers) for open source systems. We conclude with potential defenses and directions for future work...

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