sec-graph

An interactive security map + local‑LLM triage layer over any SAST.

sec-graph is not another taint engine. It takes the findings your existing static analyzer already produces — CodeQL, Semgrep, Bandit, anything that emits SARIF — binds them to a code map, enriches them with two layers no engine ships (a credentials / PII layer and an auth / unguarded verdict), and hands each finding to your own local LLM over MCP as a minimal, hash‑verified source→sink slice for triage. Everything runs locally and deterministically; no finding data leaves your machine.

Built on graphify (MIT). A small built‑in Python taint engine is kept as the fallback for when you don't have a SARIF report — it is not the product.

The layer no SAST ships: red glow = a dangerous sink reachable with no auth barrier (triage these first) · green ring = the same sink behind a real auth check. Colour = data layer; the right‑hand sidebar carries each finding's CWE + the hash‑verified source→sink slice an LLM triage call receives.

Why

Mature SAST engines are very good at finding data‑flows and very bad at two things a human (or an LLM) actually needs to triage them: "is this reachable without authentication?" and "does this path touch a credential/PII?" — plus they dump raw findings with no cheap way to feed just the relevant code to a model. sec-graph adds exactly those, on top of whatever engine you already run.

Does it actually help? (an honest benchmark)

We pre‑registered and ran a control‑vs‑treatment benchmark — a local Gemma 3n E4B triaging the same 66 CodeQL findings on three deliberately‑vulnerable Python apps, whole‑file context vs sec-graph's MCP slices, scored against hand‑labelled, independently‑audited ground truth. The honest result:

• The robust win is efficiency — sec-graph's minimal slices matched whole‑file triage quality at ~1/7th the prompt tokens and ~half the wall‑time (12× fewer tokens per finding on large files). That's what makes per‑finding triage cheap enough to run on a local model.
• Accuracy: a small, statistically‑inconclusive, repo‑dependent edge (real/FP +6 pts pooled, not significant) — reported as a signal, not a result.
• The self‑audit is honest about a limitation. sec-graph's unguarded verdict over‑reports (56% precision) when an app uses a custom auth decorator/callable not in the default barrier list, or a guard in a calling function — though it never falsely claims guarded (100%). That's configuration + a future interprocedural improvement, published, not buried.

Full methodology, numbers, and a "when it helps / when it doesn't" table: benchmark/BENCHMARK.md · pre‑registration: benchmark/PROTOCOL.md.

On a real repo (PyGoat + CodeQL's 40 findings): most web‑app findings are intra‑function, so the map is a constellation of unguarded hotspots rather than long routes — the enriched sidebar is the workhorse there, and the graph earns its keep on cross‑function/cross‑file data‑flow. Honest by design.

Install

pip install -e .          # or: uv pip install -e .
secgraph --version

Requires Python ≥ 3.11. Pulls graphify, tree-sitter, mcp, typer, pyyaml.

Quickstart

# 1. run your SAST and export SARIF
codeql database create db --language=python && \
codeql database analyze db --format=sarif-latest -o findings.sarif \
    codeql/python-queries:codeql-suites/python-security-extended.qls
#   (or)  semgrep scan --sarif -o findings.sarif

# 2. bind + enrich + map
secgraph analyze ./my-app --sarif findings.sarif   # -> graph.json, taint.json, secgraph.html
secgraph view                                       # open the interactive layered map

# 3. triage with your own LLM over MCP
secgraph serve                                      # read-only MCP server (stdio)

No SARIF? secgraph analyze ./my-app runs the built‑in Python fallback engine instead.

How it works

1. Ingest — parse SARIF (results + codeFlows) or Semgrep JSON into one normalized finding, bound to the code graph structurally (by (file, def‑line) span, never by name).
2. Enrich — add, per finding: sensitive‑data layers (credentials/PII identifiers & secret patterns on the tool‑verified flow lines) and, for Python sinks, an auth/unguarded verdict from a structural guard analysis, with an honest tri‑state (analyzed / unknown — never a false "guarded").
3. Map — a self‑contained, deterministic force‑directed HTML map: colour = data layer, red glow = unguarded, a one‑click "Critical paths" preset.
4. Triage over MCP — secgraph serve exposes read‑only tools (list_paths, get_path_slice, find_unguarded_sinks, explain_layer, get_function_taint) that hand your LLM the minimal, hash‑verified code windows for a path — never the whole repo. Run it alongside graphify --mcp (entity‑level questions stay with graphify; data‑flow paths are ours).

Enrichment in action (credentials + untrusted-input layers): a hard‑coded secret:aws-access-key-id and credentials‑tagged data‑flows in amber — surfaced from the same findings, with tags no engine emits.

The analysis core is deterministic — no LLM on the critical path; analyze twice is byte‑identical.

Scope & honest limits

sec-graph is bounded by your upstream SAST's recall — it adds no findings the engine missed. The enrichment is lexical (identifier/secret patterns on a proven flow), stronger than grep but weaker than value‑tracking taint. The auth verdict and the built‑in engine are Python‑only; other languages get the map + slices but a guard_status: unknown. See BENCHMARK.md for the measured weaknesses (including a current guard‑analysis bug); the licence/scope notes for each engine are in docs/.

Development

uv pip install -e ".[dev]"
pytest                 # 121 tests
pytest tests/contract  # the graphify contract test

Architecture: docs/architecture.md · contributing: CONTRIBUTING.md.

License & credits

MIT (see LICENSE). Built on graphify (MIT); parses CodeQL / Semgrep SARIF; uses tree-sitter. sec-graph ships no third‑party code — those are runtime dependencies / input formats.

Responsible use

Defensive and static only — sec-graph exploits nothing and ships no exploit code; the MCP triage prompts enforce a defensive framing. Audit code you own or are authorized to review, and practice coordinated disclosure. Do not use it to mass‑scan third‑party code and publish 0‑days.