Agent-Shield Blog

Gemini 2.5 Pro scored 66/100 — Grade D. 13 injection failures out of 96 tests — a 13.5% failure rate. Google's thinking model matched GPT-5.2's injection resistance but didn't surpass it. Persona hijacking and agent hijacking were fully blocked — categories where Mistral Large had a 100% failure rate. Three-model comparison included.

Mistral Large scored 53/100 — Grade D. 54 injection failures out of 96 tests — a 56% failure rate. Indirect data injection was catastrophic (15/16 failed), system prompt extraction was trivial (5/5), and every persona hijacking attempt succeeded. Head-to-head comparison with GPT-5.2 included.

GPT-5.2 scored 87/100 — Grade B. But 13 failures across 6 attack categories reveal critical weaknesses in system prompt protection, XSS output handling, and agent hijacking. We break down every failure, the real-world security impact, and what it means for production deployments.

OpenClaw just crossed 29,000 GitHub stars. It can access your email, execute shell commands, and control your smart home. But are autonomous AI agents secure? We tested 62 attack vectors across 16 OWASP categories and found critical vulnerabilities that affect every agent with broad tool access — not just OpenClaw.

We ran identical security audits against GPT-4o, Claude Sonnet 4, and Gemini 2.0 Flash using our 62-test injection suite across 16 OWASP LLM Top 10 categories. The results reveal critical differences in how each model handles prompt injection, data exfiltration, and tool misuse — and one model failed catastrophically at protecting sensitive data.