Snyk scans the tools your agent talks to.
Inkog scans the agent itself.
Both Apache-2.0 CLIs ship for AI agent security. The difference is where they look. Snyk Agent-Scan introspects the MCP tools an agent talks to. Inkog reads the agent's source code through a universal IR with 15 framework adapters and 35 detection rules.
Last verified: 2026-05-15 against public Snyk Agent-Scan v0.5.3 documentation.
Capability comparison
| Capability | Inkog | Snyk Agent-Scan |
|---|---|---|
| Agent Capability Surface (3-layer CAN/SHOULD/ENFORCED model) | ||
| Governance Score (0 to 100, graduated by effect category) | ||
| Static analysis of agent code (IR-based, taint-flow) | partial — tool descriptions only | |
| MCP server audit (6 attack categories from spec) | ||
| AGENTS.md governance verification (4-format parser) | ||
| Delegation cycle detection (DFS over agent graph) | ||
| EU AI Act Article 14 mapping (line-level) | ||
| OWASP LLM Top 10 2025 coverage | 9/10 (LLM07 May 2026) | 8/10 |
| OWASP Agentic Top 10 2026 coverage | 7/10 → 10/10 by Q3 | partial |
| Framework adapters (Python+TS) | 15 (LangChain, LangGraph, CrewAI, AutoGen, smolagents, Pydantic AI, …) | inventory-only |
| Recursive tool-loop detection (e.g. agent calling itself) | ||
| SQL injection via LLM output (taint flow) | ||
| Missing approval_func detection (EU AI Act 14.3) | ||
| False-positive rate (validated) | 0% on V14 (24 production repos, Feb 2026 internal benchmark) | ~20% per Snyk's own published "80% accuracy" claim |
| Open source (Apache 2.0) CLI | ||
| IDE auto-discovery (Claude Code, Cursor, VS Code) | partial — via MCP | |
| Brand reach (GitHub stars, May 2026) | 28★ (active, weekly) | 2,409★ |
Real benchmark: Microsoft AutoGen scan
We scanned microsoft/autogen (281 files, 58k★) with both scanners on May 15, 2026. Here's the depth gap on critical findings.
missing_oversight·_code_executor_agent.py:185CodeExecutorAgent warns if approval_func is None but proceeds anyway. LLM-generated code runs unchecked.
recursive_tool_loop·_assistant_agent.py:600Agent delegation without cycle guards or depth limits. Will infinite-loop in production with adversarial prompts.
unsigned_messages·autogen_agentchat/messages.py:1Inter-agent message communication lacks signing, enabling impersonation in multi-agent systems.
supply_chain·agbench/scenario.py:20Unverified dynamic loading of models and tools from configuration. Tool-poisoning attack surface.
overreliance·_code_executor_agent.py:260LLM output for high-risk code execution used without independent verification. Article 14(4)(b) automation bias.
When to use which
Use Snyk Agent-Scan if
- • You only need to inventory MCP servers an agent talks to.
- • You already use Snyk Open Source + Code and want one vendor.
- • You don't scan agent source code (only deps + tool configs).
Use Inkog if
- • You ship agent code (LangChain, AutoGen, CrewAI, Pydantic AI…) and need pre-deploy static analysis.
- • EU AI Act Article 14 compliance is in scope.
- • You need taint flow across the agent's tool graph.
- • You verify AGENTS.md governance claims against code.
Most mature teams run both. We integrate.
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