codejury 0.13.1

AI code security review: an adversarial diff-audit engine and an agent-driven whole-repo review methodology, with security knowledge as rich rules

codejury

AI code security review, in two paths matched to their nature:

• Diff review (coded): audit a pull request's diff for newly introduced, exploitable risks. A single balanced LLM call, or an adversarial Finder/Challenger/Judge pass for higher coverage and fewer false positives.
• Whole-repo review (agent-driven): a methodology an interactive agent (Claude Code, Codex) runs to traverse a codebase from its API entrypoints, verify issues with a real PoC, and iterate over rounds with a persistent memory. Too large for a single LLM call, so codejury ships the methodology and scaffolds the workspace rather than running a pipeline.

Security knowledge lives in rich rules (codejury/data/rules/*.md, with per-language vulnerable/secure examples), injected into the audit prompt, not buried in code.

Install

pip install codejury                 # core
pip install "codejury[anthropic]"    # or [openai] / [litellm] for a backend

Diff review

# audit a diff file
codejury audit --diff-file changes.diff

# audit a git range in a repo
codejury audit --repo /path/to/app --git-range origin/main...HEAD

# from stdin
git diff HEAD~1 | codejury audit

# adversarial mode: Finder + Challenger + Judge (higher coverage, lower FP, ~3x cost)
codejury audit --diff-file changes.diff --mode adversarial

# CI gate + SARIF
codejury audit --diff-file changes.diff --format sarif --fail-on high

Configure a backend with --provider/--model/--api-key/--api-base or the CODEJURY_API_KEY / CODEJURY_MODEL / CODEJURY_API_BASE environment variables. codejury dry-run exercises the engine with a mock provider and no key.

Whole-repo review

codejury full-review /path/to/your/repo

This scaffolds a review workspace (api/, issues/, analysis/, and a security-review-memory.md), seeds the API inventory from a deterministic scan, and prints the methodology. Run it with an interactive agent: it reads the methodology and the rules, traverses the code from its API entrypoints, records high-confidence issues with a PoC, and asks you to confirm credentials or false positives along the way. Nothing runs against production.

Findings

Each finding carries a file and line, a severity and category, a concrete exploit scenario, a recommendation, and a confidence. A false-positive filter drops test/mock-path and low-confidence noise; the model is also told not to report dependency CVEs, style notes, speculation, or config-leak-only risks.

Extending

Add a vulnerability class by dropping a new codejury/data/rules/<class>.md with the standard frontmatter (title, impact, tags, triggers) and vulnerable/secure examples. It is data; no code change needed.