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ca9 — CVE reachability analysis for Python

ca9

Stop fixing CVEs that don't affect you.

Python 3.10+ License: MPL-2.0 PyPI Minimal Dependencies Skylos A+ (99)


The problem

Your SCA tool (Snyk, Dependabot, Trivy, Grype) flags every CVE in your dependency tree. You get 60 alerts. Your team scrambles. But most of those CVEs are in code your application never imports, never calls, and never executes.

You're patching vulnerabilities in functions you don't use, in packages you didn't know you had, in code paths your app will never reach.

That's wasted engineering time. That's alert fatigue. That's how real vulnerabilities get ignored.

What ca9 does

ca9 takes your CVE list and answers one question per vulnerability: is this code actually reachable from your application?

pip install ca9[cli]
ca9 scan --repo . --coverage coverage.json
CVE ID               Package   Severity  Verdict
--------------------------------------------------------------
GHSA-cpwx-vrp4-4pq7  Jinja2    high      REACHABLE
GHSA-frmv-pr5f-9mcr  Django    critical  UNREACHABLE (static)
GHSA-mrwq-x4v8-fh7p  Pygments  medium    UNREACHABLE (dynamic)
--------------------------------------------------------------
Total: 61  |  Reachable: 25  |  Unreachable: 36  |  Inconclusive: 0

59% of flagged CVEs are unreachable — only 25 of 61 require action

36 CVEs eliminated. No manual triage. No guessing.

How it works

ca9 combines three techniques to prove whether vulnerable code is reachable:

1. Static analysis (AST import tracing) — Parses every Python file in your repo and traces import statements. If a vulnerable package is never imported, it's unreachable.

2. Transitive dependency resolution — Uses importlib.metadata to walk the dependency tree. If urllib3 is only installed because requests pulled it in, and requests is never imported, both are unreachable.

3. Dynamic analysis (coverage.py) — Checks whether vulnerable code was actually executed during your test suite. A package might be imported but the specific vulnerable function might never be called.

For each CVE:
  Is the package imported?
  ├── NO  → UNREACHABLE (static)
  └── YES → Was vulnerable code executed in tests?
      ├── NO  → UNREACHABLE (dynamic)
      ├── YES → REACHABLE
      └── No coverage data → INCONCLUSIVE

ca9 is conservative — it only marks something unreachable when it can prove it. Every verdict comes with an evidence trail and a confidence score so you can see exactly why ca9 reached its conclusion.

Why ca9 over other tools

ca9 Traditional SCA (Snyk, Dependabot, Trivy) GitHub code scanning
Reachability analysis Static + dynamic + transitive No — flags everything in the dependency tree Limited — no dynamic analysis
Submodule precision Identifies the exact vulnerable function/module Package-level only Varies
Confidence scoring 0-100 verdict-aware score with evidence trail No No
Works without SCA tool Yes — ca9 scan queries OSV.dev directly Requires its own scanner Requires GitHub
Dynamic analysis Yes — uses your existing coverage.py data No No
Runtime dependencies packaging only Heavy Hosted service
Setup time pip install ca9[cli] — one command Account, config, integration Repository setup
Output Actionable verdicts with evidence + confidence Alert list with no reachability context Alert list
CI integration JSON/SARIF output, stable fingerprints Vendor-specific dashboards GitHub-only

ca9 doesn't replace your SCA tool. It makes it useful. Snyk finds the CVEs. ca9 tells you which ones matter.

Real-world results

Django REST Framework — 37 CVEs, 19% noise

A focused library that genuinely uses most of its deps. Even here, ca9 found 7 CVEs in packages that are installed but never imported (redis, sentry-sdk, pip):

$ ca9 scan --repo /path/to/drf -v

GHSA-g92j-qhmh-64v2  sentry-sdk  low       UNREACHABLE (static)
                      -> 'sentry-sdk' is not imported and not a dependency of any imported package
GHSA-8fww-64cx-x8p5  redis       high      UNREACHABLE (static)
                      -> 'redis' is not imported and not a dependency of any imported package
...
Total: 37  |  Reachable: 0  |  Unreachable: 7  |  Inconclusive: 30

Flask app with bloated deps — 61 CVEs, 59% noise

A Flask app that imports 4 packages but has 19 pinned in requirements.txt (Django, tornado, Pygments added "just in case"):

$ ca9 scan --repo demo/ --coverage demo/coverage.json

Total: 61  |  Reachable: 25  |  Unreachable: 36  |  Inconclusive: 0

59% of flagged CVEs are unreachable — only 25 of 61 require action

Django alone brought 21 CVEs that were pure noise.

The pattern: ca9's value scales with how bloated your dependency list is — which in enterprise codebases is typically very.

Quick start

Scan installed packages (no SCA tool needed)

pip install ca9[cli]
ca9 scan --repo .

This queries OSV.dev for vulnerabilities in your installed packages. Works with any Python project. No Snyk, no Dependabot, no config files.

Add dynamic analysis for better results

coverage run --source=.,$(python -c "import site; print(site.getsitepackages()[0])") -m pytest
coverage json -o coverage.json
ca9 scan --repo . --coverage coverage.json

Analyze an existing SCA report

ca9 check snyk.json --repo . --coverage coverage.json
ca9 check dependabot.json --repo .

Format is auto-detected. Supports Snyk, Dependabot, Trivy, and pip-audit:

ca9 check snyk.json --repo .
ca9 check dependabot.json --repo .
ca9 check trivy.json --repo .
ca9 check pip-audit.json --repo .

Verdicts

Verdict What it means What to do
REACHABLE Vulnerable code is imported and was executed in tests Fix this
UNREACHABLE (static) Package is never imported — not even transitively Suppress with confidence
UNREACHABLE (dynamic) Package is imported but vulnerable code was never executed Likely safe — monitor
INCONCLUSIVE Imported but no coverage data to prove execution Add coverage or review manually

Evidence and confidence

Every verdict is backed by structured evidence. Use --show-confidence to see scores in table output, or inspect the evidence object in JSON/SARIF output.

Signal What it checks
version_in_range Is the installed version within the affected range (PEP 440)?
package_imported Is the package imported anywhere in the repo?
submodule_imported Is the specific vulnerable submodule imported?
coverage_seen Was the vulnerable code executed during tests?
api_call_sites_covered Were specific vulnerable API call sites executed in tests?
coverage_completeness_pct Overall test coverage percentage — weights dynamic absence signals
affected_component_source How was the vulnerable component identified (commit analysis, curated mapping, regex, class scan)?

Confidence scoring is verdict-directional — evidence that supports the verdict boosts the score, evidence that contradicts it lowers it. A high confidence UNREACHABLE is different from a high confidence REACHABLE.

Bucket Score Meaning
High 80-100 Strong evidence supports the verdict
Medium 60-79 Moderate evidence, reasonable certainty
Low 40-59 Weak evidence, treat with caution
Weak 0-39 Very little evidence, manual review recommended

CLI reference

ca9 scan [OPTIONS]              Scan installed packages via OSV.dev
ca9 check SCA_REPORT [OPTIONS]  Analyze a Snyk/Dependabot report

Common options:
  -r, --repo PATH                  Path to the project repository  [default: .]
  -c, --coverage PATH              Path to coverage.json for dynamic analysis
  -f, --format [table|json|sarif]  Output format  [default: table]
  -o, --output PATH                Write output to file instead of stdout
  -v, --verbose                    Show reasoning trace for each verdict
  --no-auto-coverage               Disable automatic coverage discovery
  --show-confidence                Show confidence score in table output
  --show-evidence-source           Show evidence extraction source in table output

Scan-only options:
  --offline                        Use only cached OSV data, no network requests
  --refresh-cache                  Clear OSV cache before fetching
  --max-osv-workers N              Max concurrent OSV detail fetches  [default: 8]

Exit codes:
  0  Clean — no reachable CVEs
  1  Reachable CVEs found — action needed
  2  Inconclusive only — need more coverage data

Config file

Create a .ca9.toml in your project root to set defaults:

repo = "src"
coverage = "coverage.json"
format = "json"
verbose = true

Config is auto-discovered from the current directory upward. CLI flags override config values.

Caching and offline mode

ca9 caches OSV vulnerability details (~/.cache/ca9/osv/, 24h TTL) and GitHub commit file lists (~/.cache/ca9/commits/, 7-day TTL) to reduce API calls.

ca9 scan --repo . --offline           # use cached data only, no network
ca9 scan --repo . --refresh-cache     # clear cache and re-fetch

Set GITHUB_TOKEN to avoid GitHub API rate limits when ca9 fetches commit data for affected component analysis:

export GITHUB_TOKEN=ghp_...
ca9 check snyk.json --repo .

MCP server

ca9 ships an MCP server so LLM-powered tools (Claude Code, Cursor, etc.) can run reachability analysis directly.

pip install ca9[mcp]

Add to your MCP client config:

{
  "mcpServers": {
    "ca9": {
      "command": "ca9-mcp"
    }
  }
}

Available tools:

Tool What it does
check_reachability Analyze an SCA report (Snyk, Dependabot, Trivy, pip-audit)
scan_dependencies Scan installed packages via OSV.dev
check_coverage_quality Assess how reliable your coverage data is
explain_verdict Deep-dive a specific CVE's verdict with full evidence

Library usage

import json
from pathlib import Path
from ca9.parsers.snyk import SnykParser
from ca9.engine import analyze

data = json.loads(Path("snyk.json").read_text())
vulns = SnykParser().parse(data)

report = analyze(
    vulnerabilities=vulns,
    repo_path=Path("./my-project"),
    coverage_path=Path("coverage.json"),
)

for result in report.results:
    print(f"{result.vulnerability.id}: {result.verdict.value} (confidence: {result.confidence_score})")
    print(f"  reason: {result.reason}")
    if result.evidence:
        print(f"  source: {result.evidence.affected_component_source}")

Zero heavy dependencies

ca9's core library depends only on packaging (PEP 440 version parsing) and the Python standard library. The click package is optional — only needed if you use the CLI. This means you can embed ca9 in CI pipelines, security toolchains, or other Python tools without bloating your dependency tree.

Limitations

  • Static analysis traces import statements and importlib.metadata dependency trees. Dynamic imports (importlib.import_module, __import__) are not detected.
  • Coverage quality directly impacts dynamic analysis. If your tests don't exercise a code path, ca9 can't detect it dynamically.
  • Transitive dependency resolution requires packages to be installed. Without installed deps, ca9 falls back to direct-import-only checking.
  • Python only (for now).

Development

git clone https://github.com/your-org/ca9.git
cd ca9
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v

License

MPL-2.0

About

CVE reachability analysis for Python. Stop fixing vulnerabilities that don't affect you. Static + dynamic analysis to cut SCA noise from Snyk, Dependabot, Trivy, and others.

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