Gray Swan AI

A competition run by Gray Swan with NIST's CAISI, the UK AI Security Institute and frontier labs asked 464 participants to craft indirect prompt injections that make tool-use, coding and computer-use agents take harmful actions while hiding any sign of compromise from the user. Participants made 272,000 attempts against 13 frontier models, yielding 8,648 successes; per-model success ranged from 0.5% (Claude Opus 4.5) to 8.5% (Gemini 2.5 Pro), and at least one attack succeeded against every model.

Anthropic's Claude Opus 4.6 system card reports prompt injection attack success separately for tool use (Gray Swan's ART benchmark), coding and computer use (Gray Swan's Shade adaptive attacker), and browser use (an internal Best-of-N attacker), with and without extra safeguards and across different attempt budgets. For Opus 4.6, results range from 0% in coding to 85.7% in computer use with 200 attempts and no safeguards (78.6% with extended thinking). Anthropic notes that, unlike earlier Claude models, extended thinking increased ART attack success for this model.

Anthropic describes three defenses for browser use: reinforcement learning on injected web content, classifiers that scan untrusted content, and human red-teaming including external arena-style challenges. Against an internal adaptive Best-of-N attacker with 100 attempts per environment, its chart shows Claude Opus 4.5 with the improved Claude for Chrome safeguards at 1.4% attack success (8.0% for the model alone), which the post rounds to 1%. Anthropic says this still represents meaningful risk.

Zou and colleagues (Gray Swan and collaborators; Anthropic describes the resulting benchmark as developed with the UK AI Security Institute) report a public red-teaming competition with 1.8 million prompt-injection attacks against 22 frontier agents in 44 deployment scenarios, producing over 60,000 successful policy violations. From these they build the Agent Red Teaming (ART) benchmark and find nearly all agents break within 10 to 100 queries for most behaviors, with high transfer and little correlation between robustness and model size or capability.