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Security scanner for AI agent architectures. Detects prompt injection, credential exposure, code injection, and agent-specific attack patterns.
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Static analysis security scanner purpose-built for AI agent architectures. Detects prompt injection, credential exposure, MCP server misconfigurations, code injection, and agent-specific attack patterns across your codebase -- before they reach production.
|**Focus**| Static analysis of AI agent code & prompts | General-purpose SAST with some AI/LLM rules | Runtime red-teaming of live LLM endpoints | Runtime input/output guardrails for LLM apps |
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|**AI agent-specific patterns**| 190 | Limited (general injection rules; no agent-specific categories) | N/A (probes live models, not source code) | N/A (runtime scanner, not static analysis) |
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|**OWASP Agentic Top 10 (ASI01-ASI10)**| All 10 categories, 65 patterns | Not covered | Not covered (maps to OWASP LLM Top 10, not Agentic) | Not covered |
|**SARIF output**| Yes (v2.1.0, GitHub Code Scanning) | Yes | No (JSON/HTML reports) | No |
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|**GitHub Action**| Yes (built-in `action.yml`) | Yes (`semgrep/semgrep-action`) | No | No |
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|**pre-commit hook**| Yes (built-in `.pre-commit-hooks.yaml`) | Yes | No | No |
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|**CWE mappings**| Yes (30+ categories mapped) | Yes | Limited (references CWE-1426 for prompt injection) | No |
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|**Taint analysis**| Yes (proximity-based) | Yes (cross-file dataflow in Pro) | No | No |
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|**Free / open-source**| Yes (MIT) | Community edition free; Pro is paid | Yes (Apache 2.0) | Yes (MIT) |
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**When to use each tool:**
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-**agent-security** -- You are building an AI agent (MCP servers, multi-agent systems, RAG pipelines, LLM-powered tools) and need to catch vulnerabilities in your code, configs, and prompts before deployment.
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-**Semgrep** -- You need general-purpose SAST across your full application stack (not agent-specific).
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-**Garak** -- You want to red-team a live LLM endpoint by sending adversarial probes and measuring model responses.
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-**LLM Guard** -- You need runtime input/output filtering to sanitize prompts and responses in production.
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These tools are complementary. Use agent-security in CI to catch static vulnerabilities, Garak to probe your deployed model, and LLM Guard as a runtime guardrail.
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## What It Detects
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@@ -78,14 +117,42 @@ The scanner implements detection for all 10 OWASP Agentic Security Issues:
Findings in test/fixture/example/payload directories are automatically severity-downgraded (critical→high, high→medium) since they represent lower risk.
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Findings in test/fixture/example/payload directories are automatically severity-downgraded (critical->high, high->medium) since they represent lower risk.
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### Taint Proximity Analysis
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For dangerous sinks (eval, exec, pickle), the scanner checks whether user input sources (input(), request, argv, LLM .invoke()) are within 10 lines. Direct taint escalates severity to critical.
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### Context Flow Tracing
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Detects when serialized conversation context (JSON.stringify of messages/history) flows to external API calls — a novel agent-specific attack surface.
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Detects when serialized conversation context (JSON.stringify of messages/history) flows to external API calls -- a novel agent-specific attack surface.
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name: 'Agent Security Scan'
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description: 'Scan for AI agent security vulnerabilities with 176+ detection patterns'
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description: 'Scan for AI agent security vulnerabilities with 190+ detection patterns covering OWASP ASI Top 10, MCP security, and credential exposure'
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