Inkog vs Semgrep
Semgrep was built for code. Inkog was built for agents.
Semgrep is an excellent static analysis tool for finding code-level bugs and vulnerabilities using pattern matching. Inkog focuses specifically on AI agent behavioral vulnerabilities — infinite loops, prompt injection paths, token bombing, and compliance gaps that syntax-based tools cannot detect.
Feature Comparison
| Feature | Inkog | Semgrep |
|---|---|---|
| Code pattern matching | ||
| AI agent loop detection | ||
| Prompt injection path tracing | ||
| Token bombing detection | ||
| MCP server auditing | ||
| EU AI Act compliance reports | ||
| OWASP Top 10 (traditional) | ||
| OWASP LLM Top 10 | ||
| Custom rule authoring | ||
| Agent framework adapters (15+) | ||
| Multi-agent delegation analysis | ||
| SARIF output | ||
| GitHub Actions integration |
When to Use Each Tool
Use Semgrep when...
Use Semgrep for traditional code security — finding XSS, SQL injection in web apps, buffer overflows, and enforcing code standards. Semgrep excels at pattern-based rules across any language.
Use Inkog when...
Use Inkog when your codebase includes AI agents, LLM integrations, or MCP servers. Inkog understands agent control flow, LLM data paths, and compliance requirements that syntax-based tools miss.
Frequently Asked Questions
Can Semgrep detect AI agent vulnerabilities?
Semgrep can detect some code-level issues in AI applications (like hardcoded API keys), but it cannot trace agent control flow, detect infinite loops in multi-step reasoning, or identify prompt injection paths through LLM calls. These require understanding of agent behavior, not just code syntax.
Should I use Inkog and Semgrep together?
Yes. They complement each other. Semgrep handles traditional code security (XSS, SSRF, etc.) while Inkog handles AI-specific risks (agent loops, prompt injection, compliance). Run both in your CI/CD pipeline.
Does Inkog replace Semgrep?
No. Inkog is purpose-built for AI agent security and does not cover traditional web application vulnerabilities. Think of it as an additional security layer specifically for the AI components of your stack.