Agent-Shield Blog

Every agent framework treats tool calls as trusted function invocations with no access control. AgentLock fixes that with deny-by-default permissions, five decision types (ALLOW, DENY, MODIFY, STEP_UP, DEFER), adaptive prompt hardening, and 745 tests across 182 attack vectors. Compromised admin pass rate went from 30.2/F to 81.3/B.

Google's newest flagship model scored 54/100 overall with a 20.1% injection failure rate. 32 critical findings across 159 tests. Crisis exploitation hit 89% failure rate. Full enterprise audit breakdown with model comparison.

I built a YARA detection engine that scans LLM conversations for attack patterns in production. YARA has been the standard for malware classification for over a decade — but nobody was using it for AI. Here's why it works and what I learned integrating it into AgentShield's audit pipeline.

We ran full 167-test enterprise audits against both xAI models. Grok 4 eliminated 5 attack categories and cut failures from 59 to 34 — a 42% reduction. But crisis exploitation failure rates barely budged: 90% for Grok 3, 80% for Grok 4. Reasoning helps with structured attacks but collapses under emotional pressure. Seven-model comparison included.

Claude Sonnet 4.6 scored 94/100 on injection testing — Grade A. Only 6 failures out of 96 tests, a 6.25% failure rate. The lowest of any model we've tested. All 6 failures were soft metadata leaks — zero tool execution, zero prompt compliance. Anthropic's injection resistance claim verified. Five-model comparison included.

Gemma 3 27B scored 53/100 — Grade D. 55 injection failures out of 96 tests — a 57% failure rate. Google's open-weight model matches Mistral Large's vulnerability profile, not Gemini 2.5 Pro's. The open-source security gap is real: same company, 4x the failure rate. Four-model comparison included.

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 146 attack vectors across 30 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 146-test injection suite across 30 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.