benchjack

To audit AI benchmark evaluation pipelines, you run a security scanner on the benchmark repository to reveal evaluation vulnerabilities. It parses benchmark configurations and artifact paths to produce structured findings and task mappings for hardening evaluation logic.

AI benchmark evaluation logic can be affected by vulnerabilities including data leakage, isolation issues, and prompt-injection risks. A security audit maps these risks across tasks using custom rules to generate structured findings that guide authors in improving reliability.

Yes, you can scan Dockerized benchmark environments for evaluation isolation issues. The scanner applies to benchmark repos and codebases that implement evaluation logic, including Dockerized environments and LLM-based judging, to enumerate task IDs and map vulnerabilities.

To map task IDs and config artifacts in an AI benchmark codebase, you parse the benchmark configuration and artifact paths within the repository. This enumeration is part of a security audit that produces a structured summary of vulnerabilities and task mappings.

Yes, LLM-based judging in benchmarks introduces prompt-injection risks. A security audit workflow uncovers these evaluation vulnerabilities by applying static and AI hybrid analysis with custom rules to map risk across tasks.

The best way to harden AI benchmarks against data leakage is to run an automated security audit that generates structured findings and task mappings. These guide authors in fixing evaluation vulnerabilities and improving overall pipeline reliability.