Static Analysis for AI Agents
Find design flaws, infinite loops, and security gaps before they fail you in production.
Why Inkog Verify?
Stop agent failures before they reach production.
This Happens More Than You Think
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
AI agents fail in production
Traditional tools miss agent-specific issues. Infinite loops, runaway costs, and logic flaws slip through because they require understanding how LLMs interact with your codebase.
- Unbounded API loops cause unexpected charges
- Unvalidated inputs cause unpredictable behavior
- Logic flaws create cascading failures
- Missing guardrails lead to runaway costs
Inkog Verify catches issues early
Purpose-built static analysis for AI agents. AST parsing, taint tracking, and cross-file data flow analysis designed specifically for agentic architectures.
- 20+ vulnerability patterns detected
- Cross-file taint tracking
- Works with 15+ frameworks
- Global compliance reports
Integrates in Minutes
Choose your preferred installation method.
Install Inkog
npx -y @inkog-io/cli scan .Scan Runs Instantly
Downloads the CLI on first run, caches it, and scans your codebase.
Review Results
Fix issues before shipping.
What We Detect
20+ anti-patterns and issues that cause agent failures.
Resource Exhaustion
Token Bombing
Runaway API loops that drain budgets—the #1 cause of agent cost overruns.
Infinite Loops
Missing termination conditions that cause agents to run indefinitely.
Context Window Exhaustion
Unbounded message history accumulation causing memory overflow.
Input Handling Issues
Prompt Injection
Unvalidated inputs that can hijack agent behavior or cause unexpected outputs.
Code Injection (RCE)
eval() or exec() called with LLM-generated output.
SQL Injection via LLM
LLM-generated SQL queries without parameterization.
Data Leaks & Privacy
Hardcoded Credentials
API keys and secrets embedded in source code.
Logging Sensitive Data
PII or secrets written to logs without sanitization.
Cross-Tenant Leakage
Multi-tenant isolation failures in agent memory.
Governance & Compliance
AGENTS.md Governance Mismatch
Validates AGENTS.md declarations against actual code behavior.
Missing Human Oversight
High-risk actions without approval gates. EU AI Act Article 14.
Excessive Agency
Agents with overly broad permissions. OWASP LLM08.
MCP & Multi-Agent Security
MCP Server Audit
First tool to audit MCP servers before installation.
Infinite Delegation Loops
Circular delegation in multi-agent systems.
Privilege Escalation
Unauthorized capability transfers between agents.
Works With Your Stack
One scanner for 15+ agent frameworks. Python code and JSON workflows.
Under the Hood
Built for precision. Powered by AST analysis and taint tracking.
AST Parsing
Tree-sitter based parsing for Python, JS, TypeScript
Data Flow Graph
Cross-file taint tracking with source-to-sink analysis
Control Flow
Trace execution paths to find logic flaws
Universal IR
Framework-agnostic intermediate representation
Bayesian Calibration
Self-learning confidence scores from feedback
Audit Logging
Compliance trail for regulatory requirements
Semantic Detection
Pattern matching on normalized code structure
Memory Analysis
Detect context accumulation and leakage
Your secrets never leave your machine
Source code is redacted locally before transmission. Only the sanitized logic graph is analyzed. API keys, credentials, and secrets stay on your machine.
Ready to ship reliable agents?
Start with Core for free. Upgrade to Deep when you need more.