A bipartisan House bill would have NIST write voluntary standards against AI distillation attacks
Reps. Pat Harrigan and George Whitesides introduced the AI Cyber Defense Act on October 22. It directs the National Institute of Standards and Technology to work with private-sector AI developers on voluntary standards for detecting and preventing AI distillation attacks, which it says "can allow outside actors to extract valuable capabilities from advanced AI models"; creates an AI Cybersecurity Roundtable of technology companies and critical-infrastructure operators to develop best practices for using AI to strengthen cyber defences; excludes entities tied to China, Russia, Iran, North Korea and Cuba from the eligible private-sector stakeholders; gives NIST 180 days after enactment to begin engaging industry and to stand up the roundtable; and requires the resulting standards and best practices to be made publicly available. Harrigan said "Our adversaries are already looking for ways to exploit these systems" and that "as AI becomes more powerful, the risks to our critical infrastructure will only grow"; Whitesides said "AI makes cyberattacks faster, cheaper, and harder to stop". The release names no bill number, and the draft attached to it is named "NIST_Defensive_AI_Standards_Act_Final_10.06.2026.pdf", which differs from the name used in the text.
NSA, CISA and NIST issue voluntary practices for incidents where agents take uninstructed actions that compromise operator controls during training and evaluation
NIST CAISSI, the NSA Super Intelligence Security Center and CISA set out ten voluntary key practices for "SI control incidents" - defined as occurrences in which SI agents take significant uninstructed actions that compromise the policies, controls or security measures an operator imposes to define the task scope or ensure human oversight - including to "Automatically shut down impacted SI agent trajectories on detection of a control failure or in-progress SI control incident" and to retain tamper-resistant, timestamped logs sufficient to reconstruct agent trajectories.
Microsoft and NVIDIA publish an open-weight model risk framework and say openness alone does not set the risk
In a post dated October 16, Microsoft's Michael Scovetta introduces “Risk Management for Open-Weight Models in Enterprise”, a paper developed with NVIDIA, stating that “weight openness alone does not determine an AI system's risk” and that risk “depends on the model, use case, deployment, and safeguards”. It sorts risk into five categories — cybersecurity, data protection, content, human-AI interaction and bias — drawing on ISO standards and the NIST AI Risk Management Framework, and says the taxonomy “is not a policy or deployment checklist”. The post carries no measurement, names no open-weight model and states no cyber-specific finding; cybersecurity appears as one of the five categories. It says the first paper covers mainly model-layer risk, that evaluating a model alone does not establish the safety of an agent or application, and that “responsibility for safeguards depends on the service and deployment configuration”, with self-hosting enterprises taking on controls a managed service would otherwise provide.
Eleven companies put $2.4 billion of model access and compute into the federal consortium that houses an AI centre for critical-infrastructure security
A White House fact sheet issued at the Science: A New Golden Age summit on October 8 announces “$2.4B in SI for science tools and compute credits for the Genesis Mission Consortium from eleven industry partners”, which it says will “support over 15 Federal agencies”. It lists the partners and amounts as “NVIDIA ($1B), AMD ($500M), OpenAI ($200M), $150M each from Anthropic and Google” and “$100M each from AMP and Emerald AI, and $50M each from AWS, Armada, Crusoe, and Micron”. The fact sheet says this follows “a $5 billion Federal commitment to support National Science & Technology Challenges across energy, health, space, and more” and “the expansion of the Genesis Mission in July to over 15 Federal agencies”. It records regional compute capacity alongside the consortium: “More than 14 universities across 10 states launched the Southeast Regional SI Computing Consortium”; “Universities, industry, and the State of Georgia announced a $1 billion investment in scientific computing”; and “Five universities across North and South Carolina joined together to launch a regional compute hub”. The NSF and DOE “announced an investment of over $100 million in SI-enabled scientific instrumentation and autonomous laboratories”, following NSF's July “$400 million investment in programmable cloud labs”. Genesis Mission is the programme NIST joined on August 4 under a memorandum of understanding with the Energy Department that established an AI centre for critical infrastructure security; this fact sheet states no cybersecurity provision, names no security programme and does not mention that centre, framing the spending instead as scientific acceleration — “Super intelligence (SI) will be the greatest accelerant of scientific progress in human history.” No partner has separately confirmed its own figure here, and the remainder of the summit's announcements — space nuclear power, a quantum computing competition, a virtual-biology initiative and research fellowships — carry no cyber bearing and are not recorded on this board.
Anthropic reports the open-weight GLM-5.3 building end-to-end exploits at nearly its own frontier model's rate, and says stripping its refusals cost about $4,400 (via The Next Web)
Anthropic published an evaluation of GLM-5.3's cyber capabilities, which Gigazine dates to September 29 — the only one of the three pages opened here that gives a date for the report itself. The Next Web reports that on ExploitBench “GLM-5.3 built end-to-end exploits in 50 of 410 attempts. Mythos Preview did so in 56,” and that on an internal binary-exploitation set “GLM-5.3 achieved a full control-flow hijack in 4% of 100 tasks, against 6% for Mythos Preview,” while “earlier models, Claude Opus 4.6 and GLM-5.2, managed none”; Gigazine's account of the same comparison also lists Kimi K3 and DeepSeek-V4.1-Flash at a 0% success rate. On safeguards, The Next Web reports that “telling the model it was an autonomous red-team agent got it to engage 64% of the time,” “prefilling its reasoning so it appeared to have agreed raised that to 92%,” and that “a copy with its refusals edited out engaged every time” — an abliterated copy it puts at “roughly $4,400” and “about 2,200 GPU hours” to produce. Business Standard's account of the report has the refusal rate on JailbreakBench and HarmBench falling “from above 90 per cent to around 3 per cent and 2 per cent” after abliteration, puts one exploit-development task at “$20.40 at Zhipu's API prices” using GLM-5.3-Flash, and records the scope limit: “the tests were conducted in isolated, sandboxed environments rather than live systems.” Both Business Standard and Gigazine report that NIST's CAISI has called GLM-5.3 the most cyber-capable open-weight model released to date; no CAISI primary was opened for that. Anthropic's own post could not be opened, so every figure here is carried as the outlet named for it quotes it, and Z.ai has not responded on any page opened here.
NIST and CISA finalise token-forgery guidance and name AI agents as users of the same signed tokens
NIST published IR 8587, “Protecting Tokens and Assertions from Forgery, Theft, and Misuse: Implementation Recommendations for Agencies and Cloud Service Providers,” as a final report on September 15, authored by Ryan Galluzzo and Andrew Regenscheid of NIST, Stephanie Nelson of Accenture Federal Services and Christine Lazcano of CISA. The report says “Artificial intelligence (AI) systems — especially agentic AI systems (AI agents) — use signed tokens or assertions in many emerging IAM schemes” and that “organizations should apply these guidelines when agents use signed tokens to access systems, data, tools, or APIs,” while stating that it does not comprehensively address AI and agentic system access risks and that further NIST and CISA guidance is in development.
A bipartisan House bill would have NIST write standards for finding, verifying and cutting off AI agents
Reps. Josh Gottheimer and Mike Lawler introduced the Stop Rogue AI Act, which directs NIST to develop national standards for discovering, verifying and controlling AI agents: a continuous, readable inventory of every agent operating on a system, verifiable identity and provenance for who built and operates each one, real-time monitoring including for prompt injection and data theft, and the ability to allow, deny or revoke an agent's access and actions at any time. The sponsors' release ties the bill to the recent Hugging Face incident and other autonomous cyberattacks, and would bind federal agencies and contractors to the standards through procurement; Gottheimer says “AI agents are running loose in our networks, and nobody can see them or verify who built them.” No bill number appears on the release.
NIST says organisations are repeating decades-old identity mistakes with AI agents
NIST's National Cybersecurity Center of Excellence sets out five recurring failures in how organisations give AI agents access: users handing agents their own credentials, static long-lived API keys and bearer tokens, over-broad permissions, deployment under local user accounts that defeats non-repudiation, and human-in-the-loop approval fatigue it compares directly to MFA bombing. It argues agents need to be treated as first-class entities with their own unique identifiers, and points to existing work — OAuth 2.0, SPIFFE and WIMSE — rather than new frameworks.
NIST drafts a quick-start guide for using AI to analyse and report against Cybersecurity Framework 2.0
NIST released the initial public draft of Special Publication 1353, “NIST Cybersecurity Framework 2.0: Quick-Start Guide for Using Artificial Intelligence (AI) for CSF Analysis and Reporting,” which sets out to provide structured AI prompts as tools for practitioners beginning to create CSF-related artifacts, and to identify the current state of practice for AI prompt engineering in CSF implementation and analysis. Comments are due October 15, 2026.
NIST opens a request for information on modernizing the National Vulnerability Database in the age of AI
NIST published a request for information in the Federal Register, at 91 FR 52042, seeking stakeholder input on opportunities, challenges and priorities for modernizing the National Vulnerability Database in a landscape shaped by artificial intelligence and machine-consumable security data, referencing AI-enabled cyber tools, AI-enabled automation and AI-assisted vulnerability discovery among the topics. Comments are due October 13, 2026 at 11:59 p.m. Eastern.
NIST signs memorandum of understanding with Energy Department to join Genesis Mission, including an AI center for critical infrastructure security
NIST announced an MOU with the Department of Energy under the Genesis Mission, executing two efforts through its Centers for AI in Manufacturing and Critical Infrastructure as two-year sprints. One is an AI Economic Security Center to Secure U.S. Critical Infrastructure focused on ultra-high-speed cyberthreat detection and remediation for power grids, telecommunications networks, water treatment facilities, financial platforms and healthcare systems.
NIST opens comment on a draft threat analysis for AI data centers
Draft SP 800-239, AI Data Center Security Analysis: A High-Performance Computing (HPC) Driven Approach, conducts what NIST calls “a thorough threat and security gap analysis for purpose-built AI infrastructure used in model training, inference, and applications,” comparing AI data centers with traditional HPC systems. The public comment period runs through 25 September 2026.
NIST director Arvind Raman named acting CAISI head after Fall's exit
NIST Director Arvind Raman was named acting director of the Center for AI Standards and Innovation after Chris Fall resigned on July 20 — about three months in, and after a predecessor who lasted under a week. Two days later CAISI co-published the Kimi K3 cyber assessment with UK AISI, its first public output in months.