For Engineering Teams

Prevent Your Coding Agent from Deleting the Database.

Hidden payloads in issues and PRs. Your agent executes them.

Learn About Verify

The PromptPwnd Attack

Malicious payload hidden in issue → Agent executes it

GitHub IssueUntrusted
Agent Reads
Inkog blocks taint flow
Code Generation
exec() SinkRCE

Deep Taint Analysis

Track untrusted input from external sources and block it from reaching dangerous sinks.

GitHub Issue content
PR descriptions
External API responses

Taint Tracking

Track data flow from untrusted sources through your agent's entire execution path.

Sink Detection

Identify dangerous operations like exec(), eval(), and subprocess that attackers target.

inkog-cli
inkog scan pr-agent.py
Scanning agent code...
CRITICALissue.body flows to subprocess.run()
agents/pr/executor.py:249

Why Engineering Teams Choose Inkog

RCE Prevention

Stop remote code execution before attackers can exploit your agents.

CI/CD Integration

Scan agent code in your pipeline. Block vulnerable code from shipping.

Shift Left Security

Find vulnerabilities during development, not in production incidents.

Frequently Asked Questions

What is indirect prompt injection?+

Indirect prompt injection occurs when attackers embed malicious instructions in content an AI agent will read—like GitHub issues or PRs. The agent unknowingly executes these commands.

How does Inkog prevent RCE in coding agents?+

Inkog tracks untrusted input from sources like GitHub Issues through your agent's code. It blocks paths where tainted data can reach dangerous sinks like exec() or subprocess.run().

What is taint analysis?+

Taint analysis tracks data from untrusted sources through your code to identify when it reaches sensitive operations. Inkog applies this to detect when AI agents can be manipulated through their inputs.

Ready to secure your coding agents?