codejury 0.10.0

General-purpose Application Security AI audit framework -- five-layer architecture, capabilities as first-class data

codejury

An AI security auditor for code whose knowledge lives in versioned YAML, not in prompts. It reviews a diff or a whole repository against the OWASP ASVS and reports a verdict per dimension -- both what is vulnerable and what is verified safe.

The name is the core idea: code goes before a "jury" of adversarial roles -- Finder / Challenger / Judge -- that argue and converge on a verdict.

Why it is built this way:

• Knowledge is data. Each OWASP ASVS area (and now OWASP LLM Top 10 areas) is a YAML capability (safe patterns + anti-patterns, with CWE and examples) -- versioned, reviewable in a PR, and editable by non-engineers. The framework core stays small.
• Verdicts, not just alerts. Every capability yields SECURE / VULNERABLE / PARTIAL / NOT_PRESENT, so a report shows what was checked and passed, not only what failed.
• Composable. Four orchestration strategies, four model backends, and diff / repo inputs are chosen per run -- mix and match.

Install

pip install codejury                 # core + CLI
pip install 'codejury[anthropic]'    # the provider you'll use: anthropic | openai | litellm

Quickstart

# CI gate: exit 1 if a high-severity issue is found
git diff origin/main... | codejury audit --fail-on high -

# Post inline review comments on a GitHub pull request (needs GITHUB_TOKEN)
git diff origin/main... | codejury audit --github your-org/your-repo#123 -

# No API key needed -- prove the pipeline runs end to end with mock layers
codejury dry-run

# A real audit: set a key, then review your staged changes
export ANTHROPIC_API_KEY=sk-ant-...
git diff | codejury audit --provider anthropic

Commands

Command	What it does
codejury dry-run	Run the mock pipeline with no key (smoke test).
codejury audit [diff]	Audit a unified diff from a file or stdin (-).
codejury scan <dir>	Audit a whole directory tree, capability by capability.
codejury run <task>	Run a named task preset (see Tasks).
codejury eval	Score the golden cases; report precision / recall / F1, overall and per capability.

Shared flags: --orchestrator {single,pipeline,debate,reflexion,challenge,taint,adaptive}, --provider {anthropic,openai,litellm}, --model, --format {text,markdown,json,sarif}.

audit/scan take --baseline <report.json>: save a JSON report of the target branch, then on a PR report only findings new since it (matched by a line-tolerant fingerprint, so shifted code is not re-reported). Pair with --fail-on to gate CI on new issues only:

git checkout main && codejury scan . --format json > baseline.json
git checkout pr-branch && codejury scan . --baseline baseline.json --fail-on high

--orchestrator taint adds a data-flow gate: after the verifier rules, it clears an input_validation finding only when static provenance analysis proves the value reaching the sink is constant, sanitized, or trusted (using cross-file caller/callee context). It downgrades only on positive proof, so it removes false positives without dropping real findings.

--orchestrator adaptive keeps cost down: it runs the single verifier first and escalates to a full debate only for artifacts worth it -- any VULNERABLE verdict (verify a flag before reporting) or a low-confidence PARTIAL/UNKNOWN one. Clean, confident files pay a single call; only the rest pay for debate.

--format sarif emits a SARIF 2.1.0 log (validates against the official schema) for CI and security dashboards: each problem with a code location becomes a result carrying its capability (as the rule id), CWE, and a precise location.

Findings in known-noise categories (availability/DoS, rate limiting, memory safety outside C/C++) are dropped by versioned rules in codejury/data/suppressions.yaml; disable with --no-suppress.

codejury eval takes --dataset <dir> (golden YAML directory), --split <name> (score only cases tagged with that split:, e.g. a held-out set), and --format {text,json} -- the JSON report is a stable schema (overall plus per-capability confusion matrix and precision / recall / F1).

Runs are deterministic: providers query at temperature 0, and audit / scan cache each verdict on a hash of the normalized code, the in-scope capability versions, and the orchestration. Re-auditing unchanged code returns the recorded verdicts without re-querying the model; editing a capability YAML changes its fingerprint and invalidates affected entries. Pass --no-cache to always re-query.

# Multi-round adversarial debate, rendered as Markdown
git diff | codejury audit --orchestrator debate --format markdown - > report.md

# Deep whole-repo scan, scoped to a few capabilities to bound the cost
codejury scan ./myrepo --only secrets,input_validation,crypto

Configuration

Provider keys are read from the environment (codejury does not auto-load .env -- copy .env.example and source it):

Variable	Used by
ANTHROPIC_API_KEY	--provider anthropic
OPENAI_API_KEY	--provider openai
CODEJURY_API_BASE / CODEJURY_API_KEY / CODEJURY_MODEL	defaults for --api-base / --api-key / --model (any provider)

The CODEJURY_* overrides make a LiteLLM proxy a one-liner:

# with CODEJURY_API_BASE / CODEJURY_API_KEY / CODEJURY_MODEL in a sourced .env
git diff | codejury audit --provider litellm -

Tasks

A task is a named preset (capabilities + orchestrator + provider + model). It lives in a YAML file; the API key always stays in the environment.

# mytasks/proxy_scan.yaml  ->  codejury run proxy_scan --tasks mytasks
name: proxy_scan
orchestrator: debate
provider: litellm
model: your-alias
api_base: https://litellm.example.com   # key from CODEJURY_API_KEY
capabilities: [authn, input_validation, secrets]   # omit to check all

Capabilities

The library covers all 11 OWASP ASVS areas plus a growing set of OWASP LLM Top 10 capabilities, one YAML each under codejury/data/capabilities/. These ids are what --only and a task's capabilities: accept:

authn · authz · session · input_validation · output_encoding · crypto · secrets · data_protection · error_logging · business_logic · dependency_config · prompt_injection · insecure_output_handling · excessive_agency

To tune for your codebase, edit these files (add patterns / sharpen wording) -- no code change needed.

Architecture

Layer 5  Task            preset: source + capabilities + orchestrator + agents
Layer 4  Capability      YAML domain knowledge (authn / authz / ...)
Layer 3  Orchestrator    strategy (single / pipeline / debate / reflexion)
         Source          input (diff / repo / function)
         Agent           role (finder / challenger / judge / verifier)
Layer 2  Provider        model backend (anthropic / openai / litellm / mock)
Layer 1  Infrastructure  cross-cutting utilities (json parsing, retry, ...)

Layers talk only through typed data, and each is an abstract base class plus implementations, so the axes (task / orchestration / model / input) compose independently.

Limitations

• Prompts are a first pass. Expect false positives and misses on real code. Tune by editing the capability YAML and growing the golden set; measure the effect with codejury eval.
• Local-pattern checks are sharper than data-flow ones. Capabilities judged from one spot (weak crypto, hardcoded secrets) are reliable; taint / data-flow ones like path traversal over-flag in single-file review because the verifier can't see whether a value is attacker-controlled. Mitigations that add context but do not fully solve it: scan --callers (where this file's functions are called) and scan --callees (the called code it delegates to, so a sink in another file is visible) -- pair them for both directions; --orchestrator challenge (a recall-safe refutation pass that drops only provably-safe flags); --only to scope; or --orchestrator debate. Real taint precision still needs data-flow analysis, not model skepticism.
• scan cost scales as files x capabilities. It is a periodic deep audit, not a quick check -- scope it with --only. Day to day, audit the diff.

Development

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest