Find design flaws, infinite loops, and security gaps before they fail you in production.
Inkog provides diff-based security scanning for CI/CD pipelines. Compare current scans against a baseline to detect only new vulnerabilities introduced by pull requests. Fail builds only on security regressions, not pre-existing issues. Perfect for GitHub Actions, GitLab CI, and Azure DevOps pipelines. Track security improvements over time with risk score deltas.
Inkog detects Token Burning attacks in LangChain and LangGraph agents where unbounded API loops drain your budget. Static analysis for LLM API calls in while True loops without exit conditions. CWE-770 vulnerability detection for LangChain, OpenAI, and enterprise AI applications.
Inkog scans n8n no-code automation workflows for infinite loops in agentic systems. Detects missing termination guards like Max Revisions checks in Writer-Reviewer agent cycles that cause stuck processes and 100% CPU resource drain. CWE-835 vulnerability detection for n8n, Flowise, and Langflow AI workflows.
Inkog traces data flow in CrewAI agents to detect unvalidated code execution vulnerabilities. Identifies dangerous patterns like eval() calls with user or LLM-generated input without proper validation. CWE-94 vulnerability detection for CrewAI, AutoGPT, and Python AI agents.
Stop agent failures before they reach production.
A multi-agent system shipped without termination checks:
while True:
response = llm.complete(user_query) # No exit condition
# Result: Unexpected API charges in hoursAgent Development Lifecycle
Traditional tools miss agent-specific issues. Infinite loops, runaway costs, and logic flaws slip through code review because they require understanding how LLMs interact with your codebase.
Purpose-built static analysis for AI agents. AST parsing, taint tracking, and cross-file data flow analysis designed specifically for agentic architectures.
Choose your preferred installation method.
docker run -v $(pwd):/app ghcr.io/inkog-io/inkog:latest /appThe container scans your mounted directory and outputs results.
Fix issues before shipping.
20+ anti-patterns and issues that cause agent failures.
Runaway API loops that drain budgets—the #1 cause of agent cost overruns.
Missing termination conditions that cause agents to run indefinitely.
Unbounded message history accumulation causing memory overflow.
Unvalidated inputs that can hijack agent behavior or cause unexpected outputs.
eval() or exec() called with LLM-generated output.
LLM-generated SQL queries without parameterization.
API keys and secrets embedded in source code.
PII or secrets written to logs without sanitization.
Multi-tenant isolation failures in agent memory.
Validates AGENTS.md declarations against actual code behavior. Catches when agents exceed stated boundaries.
High-risk actions without approval gates. EU AI Act Article 14 compliance.
Agents with overly broad permissions. OWASP LLM08 detection.
First tool to audit MCP servers before installation. Analyze tool permissions, data flow risks, and input validation.
Detect circular delegation patterns in multi-agent systems that cause runaway execution.
Identify unauthorized capability transfers between agents in A2A communication.
One scanner for 15+ agent frameworks. Python code and JSON workflows.
Same detection rules across all frameworks. No configuration needed.
Automated mapping to global AI governance frameworks.
Global Standard
US Framework
European Union
Top 10
Source code is redacted locally before transmission. Only the sanitized logic graph is analyzed remotely. API keys, credentials, and secrets stay on your machine.
Built for precision. Powered by AST analysis and taint tracking.
Tree-sitter based parsing for Python, JS, TypeScript
Cross-file taint tracking with source-to-sink analysis
Trace execution paths to find logic flaws
Framework-agnostic intermediate representation
Self-learning confidence scores from feedback
Compliance trail for regulatory requirements
Pattern matching on normalized code structure
Detect context accumulation and leakage
Join teams using Inkog to build agents that actually work in production.