The OWASP Top 10 for Large Language Model Applications, published in October 2025, defines the most critical security risks for systems that integrate LLMs. It covers prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, and five more categories. If you ship a product that calls an LLM API, these risks are your risks.
We built PatchOps Guard’s LLM Guard lane specifically to cover all 10 categories. Then we checked what the competition offers. The gap is stark.
The 17-rule LLM Guard
Our LLM Guard lane ships 17 detection rules mapped to the OWASP LLM Top 10:
- LLM01 — Prompt Injection: detects raw string concatenation into prompt templates, missing input sanitization before LLM calls, and system/user prompt boundary violations.
- LLM02 — Insecure Output Handling: flags LLM output rendered as HTML without escaping, executed as code via
eval(), or passed to shell commands. - LLM03 — Training Data Poisoning: identifies fine-tuning pipelines that ingest unvalidated external data, missing data provenance checks.
- LLM04 — Model Denial of Service: detects unbounded input lengths, missing token limits, and recursive LLM calls without depth limits.
- LLM05 — Supply Chain: scans for known-vulnerable model weights, unsafe pickle deserialization in model loading (via our HuggingFace pickle scanner), and unsigned model artifacts.
- LLM06-10: sensitive information disclosure (PII in prompts), insecure plugin design (tool-use without permission boundaries), excessive agency (auto-execute without human approval), overreliance (missing confidence thresholds), and model theft (exposed model endpoints without authentication).
Competitor comparison
We evaluated Snyk, Socket.dev, Endor Labs, and Semgrep OSS against the same 10 categories:
- Snyk: covers supply chain (LLM05) via container/dependency scanning. No dedicated rules for LLM01-04 or LLM06-10. Score: 1/10.
- Socket.dev: focuses on malicious packages (LLM05 partial). No LLM-specific detection. Score: 0.5/10.
- Endor Labs: reachability analysis for traditional CVEs. No LLM category coverage. Score: 0/10.
- Semgrep OSS: community rules exist for prompt injection (LLM01) and insecure output (LLM02). No coverage for LLM03-10 without custom rules. Score: 2/10.
- PatchOps Guard: 17 rules covering all 10 categories. Score: 10/10.
Beyond detection
Detection is the baseline. PatchOps Guard also generates AI-assisted fixes for LLM Guard findings — adding input validation, output escaping, token limits, and permission boundaries. Each fix goes through the same 5-stage sandbox pipeline as CVE patches. No unverified code reaches your main branch.
If you are building with LLMs, you need LLM-specific security tooling. Explore the full rule set and benchmark at patchguard.ai/benchmark.