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CategoryAll Malware 1342 Vulnerability 1296 Exploit / PoC 593 Ransomware 282 Threat actor 100 Data breach 65 Advisory 59 Phishing & fraud 56 Industry 54 Policy & legal 42 AI research 33 Research 30 Tools 27 AI safety & security 26

SeverityAll critical 1432 high 1815 medium 560 low 129 info 117

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent SpaceSep 15, 04:50 UTC · 2d agoAI safety & security

CISOs Race to Control AI Agents Without Destroying Their Value

Team8 survey: 78% of CISOs name AI and agent security their biggest pain point as over-privileged agents expand attack surface.

Team8's annual CISO Village survey reports that 78% of security leaders cite AI and agent security as their biggest pain point, twice the second-ranked concern (39%), while 71% are experimenting with or augmenting security tools using AI agents. Team8 CISO Tim Brown warns that employee-built agents created with tools like Claude Code, Cursor and Codex can take unintended harmful actions, such as poking around production systems, because prompt imprecision combines with non-deterministic model behavior. Brown recommends building guardrails into the agent development process to limit where agents can go and what they can do, without destroying business utility. He also urges greater transparency and experience sharing among security leaders facing the same agent security problems.

SecurityWeekSep 14, 10:30 UTC · 3d agoAI safety & security

We have a year to fix security everywhere

Blog post warns that cheap open-weight GLM 5.3-flash, once abliterated, could enable mass AI-driven vulnerability exploitation, urging industry-wide patching now.

An essay argues that Z.ai's open-weight GLM 5.3-flash—runnable locally on roughly $6k consumer hardware at 20-45 tokens/second—combined with 'abliterated' variants from groups like DeAlignAI that score 0% on HarmBench-320 puts dangerous hacking capability in nearly anyone's hands. GLM 5.3 scores 84.5% on CyberGym and 54.4% on ExploitBench, versus GPT-6 Astra's 100% and GPT-5.6 Sol's 78.5%, and the author cites evidence of frontier models exploiting real-world infrastructure. The author calls for using LLMs (Project Glasswing, Daybreak) to find and fix vulnerabilities industry-wide before adversaries weaponize cheap open models.

Hacker News · securitySep 8, 04:48 UTC · 9d agoAI safety & security HN 278↑ · 292 comments

Google’s $10,000 refund test shows why AI agents need zero trust

Google released an open-source zero-trust reference architecture for AI agents defending against prompt injection via signed database writes, gVisor sandboxing, and deterministic gating.

Google's demonstration, built on the Agent Development Kit (ADK) and Gemini, shows a customer support agent that could be manipulated into issuing a $10,000 refund on a $149 order and exposing environment variables via AI-generated Python. The architecture adds three security layers outside the model: cryptographic signatures on database writes verified via Cloud KMS backed by Cloud HSM, gVisor sandboxing of generated code with network egress disabled, and a Semantic Gateway applying deterministic checks to prompts and tool calls. It treats system prompts as insufficient boundaries because prompt injection, prompt tuning, or model updates can change agent behavior.

Help Net SecurityAug 18, 00:00 UTC · Aug 18, 2026AI safety & security