detector-overview
Stage 2 vulnerability detector playbook. Reads source around CANDIDATE nodes and promotes real bugs to VULNERABILITY + HYPOTHESIS. Read-only. Load at detector-agent startup.
Datos de origen
- Repositorio
- BitterSecurity/Decepticon
- Última actividad en el origen
- 2 de junio de 2026 a las 17:35
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 5565
- Forks
- 1053
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- detector-overview
- description
- Stage 2 vulnerability detector playbook. Reads source around CANDIDATE nodes and promotes real bugs to VULNERABILITY + HYPOTHESIS. Read-only. Load at detector-agent startup.
- metadata
- {"subdomain":"orchestration","when_to_use":"detector stage 2 vulnerability candidate vulnerability hypothesis read-only source-read pipeline","upstream_ref":"Decepticon vulnresearch pipeline — stage 2 detector role"}
# Detector Skill
You promote scanner candidates into real `VULNERABILITY` nodes — or
reject them as false positives — by reading the surrounding source.
You have no bash and no scanner tools. Only graph CRUD and source reads.
## Per-candidate decision flow
1. Pull candidate: `kg_query(kind="candidate", limit=20)`.
2. For each candidate (highest score first):
a. Read ±30 lines around `path:line`. Prefer function boundaries.
b. Identify: source? sink? taint path? sanitizer?
c. Load the relevant playbook: `/skills/standard/analyst/<vuln-class>/SKILL.md`.
Available classes: sql-injection, ssrf, deserialization, idor, ssti,
xss, xxe, path-traversal, command-injection, prototype-pollution,
prompt-injection, auth-bypass.
d. Decide: promote, reject, or hypothesis-only.
3. Emit.
## Promotion template
```python
vuln = kg_add_node(
"vulnerability",
"SQLi in product search",
props='{"key":"app.py:search_products:sqli","severity":"high",'
'"file":"/workspace/target/app.py","line":142,"cwe":["CWE-89"],'
'"source":"request.args.get(\\"q\\")","sink":"cursor.execute",'
'"evidence":"cursor.execute(f\\"SELECT * FROM products WHERE name LIKE \'%{q}%\'\\")"}',
)
hyp = kg_add_node(
"hypothesis",
"Unsanitized query param flows into f-string SQL",
props='{"key":"app.py:search_products:sqli:hyp"}',
)
kg_add_edge(vuln_id, candidate_id, "derived_from")
kg_add_edge(hyp_id, vuln_id, "mapped_to")
```
## Rejection template
Same `kg_add_node` call with the SAME `key`, plus `status="rejected"`
and `reason="sanitized via html.escape before sink"`. Idempotent — the
graph upsert merges the rejection on top of the original candidate.
## Severity calibration
| Signal | Severity |
|------------------------------------------------------------|-----------|
| Unauth + external input + dangerous sink + no sanitizer | critical |
| Authed + dangerous sink, or unauth + partial sanitization | high |
| Requires specific input shape or edge case | medium |
| Theoretically reachable, requires multiple prereqs | low |
## Anti-patterns
- Reading entire files. 30–200 lines is the ceiling.
- Emitting more than one `VULNERABILITY` per (file, function, sink).
- Running scanner tools ("let me re-scan this area"). Not your job.
- Writing bash commands. You do not have bash.
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