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Vuln scan

Skill air-gapped/skills/.claude/skills/vuln-scan

Claude Code plugin marketplace — 58 installable reference skills across vLLM/SGLang inference, Kubernetes & Harvester, GPU host bring-up, observability, security, and agent workflows.

Install
npx -y skills add air-gapped/skills --skill vuln-scan

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[2/4 defending-code] Static source-code vulnerability scan. Second step of the find-and-fix loop (/threat-model -> /vuln-scan -> /triage -> /patch), and the usual entry point when no threat model exists. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes (build + run + sanitizer), see HARNESS.md. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", "audit this code for vulnerabilities", or as the step between /threat-model and /triage.

SKILL.md

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/vuln-scan

Second leg of the defending-code loop (/threat-model/vuln-scan/triage/patch). Static vulnerability review of a source tree. Produces VULN-FINDINGS.json (+ a human-readable .md) that /triage ingests directly. Reads THREAT_MODEL.md if step 1 ran, and falls back to its own recon if not — so this is a valid place to start the loop.

This skill does not execute code. It reads source and reasons about it. For execution-verified findings (sanitizer crashes, reproducing PoCs), point the user at an autonomous execution harness — see HARNESS.md in this skill directory for the reference C/C++ + AddressSanitizer pipeline this group was extracted from.

Tool fallbacks. Prefer the dedicated Glob and Grep tools. Some sessions do not provision them — allowed-tools is a permission filter, not a loader, so listing them here does not make them appear. When Glob/Grep are unavailable, fall back to the read-only Bash commands whitelisted above: rg --files <scope> / ls -R for enumeration, rg -n / grep -rn for search, wc / head / file for sniffing. These are the ONLY permitted Bash commands; do not write helper scripts or pipe target content into a shell interpreter.

Arguments

  • <target-dir> (required) — directory to scan. Relative or absolute.
  • --focus <area> — scan only this focus area (repeatable). Skips recon.
  • --single — no subagent fan-out; one sequential pass. Use on tiny targets or when debugging the prompt.
  • --extra <file> — append the contents of <file> to the review brief (after the category list). Use to add org-specific vulnerability classes, compliance checks, or stack-specific patterns. Plain text; same shape as the category blocks below.
  • --no-score — skip the Step 3b confidence pass (saves a round of subagents). Findings keep the scanner's self-reported confidence only.

Step 1 — Scope

  1. Resolve <target-dir>. If it doesn't exist or has no source files, stop with an error.
  2. Look for <target-dir>/THREAT_MODEL.md. If present, parse its section 3 "Entry points & trust boundaries" table and section 4 "Threats" table for focus areas and threat classes. This is the preferred scoping input. Also parse its section 2 "Assets" table and section 1 system context into two bullet lists — assets and deployment_facts — for the review brief: severity calibration needs what the system protects, not just where input enters.
  3. If no THREAT_MODEL.md and no --focus: do a quick recon — list the source tree, read entry points and dispatch code, and propose 3-10 focus areas using the pattern <subsystem> (<function/file>) — <key operations>. During recon, also read any deploy manifests in the target (Helm chart, k8s YAML, compose file, Dockerfile env) and note as deployment_facts what is actually there: mounted secrets, auth in front, sessions/cookies, persistent state, tenancy. "Stateless, no auth, chart mounts no secret" changes every severity downstream.
  4. If --focus was given, use exactly those.

Tell the user the focus areas you'll scan and the source-file count before fanning out.

Step 2 — Fan out

Unless --single, spawn one Task subagent per focus area in parallel. Cap at 10 concurrent. Each subagent gets the review brief below with its focus area filled in. On tiny targets (<15 source files), fall through to --single automatically.

Review brief (per subagent)

You are conducting authorized static security review of source code. Your
focus area: **{focus_area}**. Other agents cover other areas; duplication
is wasted effort.

TARGET: {target_dir}
TRUST BOUNDARY: {from THREAT_MODEL.md section 3, or "untrusted input → process memory"}
ASSETS (what is worth protecting here):
{assets bullets from THREAT_MODEL.md section 2, or "(unknown — name the
 asset you assume for each finding)"}
DEPLOYMENT FACTS (what is actually deployed/mounted):
{deployment_facts bullets, or "(unknown — check deploy manifests in the
 target before assuming secrets, auth, or sessions exist)"}

TASK: read the source in your focus area and identify candidate
vulnerabilities. This is static review — do NOT build, run, or probe
anything. Reason from the code.

REPORTING BAR: report anything with a plausible exploit path. Skip style
concerns, best-practice gaps, and purely theoretical issues with no attack
story at all — but if you're unsure whether something is real, REPORT IT
with a low confidence score rather than dropping it. A downstream triage
step does the rigorous verification; your job is to not miss things.

WHAT TO LOOK FOR:

  MEMORY SAFETY (C/C++ and unsafe/FFI blocks) — HIGH VALUE:
  - heap-buffer-overflow / stack-buffer-overflow / global-buffer-overflow
  - heap-use-after-free / double-free
  - integer overflow feeding an allocation or index
  - format-string bugs
  - unbounded recursion or allocation driven by untrusted size fields

  INJECTION & CODE EXECUTION — HIGH VALUE:
  - SQL / command / LDAP / XPath / NoSQL / template injection
  - path traversal in file operations
  - unsafe deserialization (pickle, YAML, native), eval injection
  - XSS (reflected, stored, DOM-based) — but see React/Angular note below

  AUTH, CRYPTO, DATA — HIGH VALUE:
  - authentication or authorization bypass, privilege escalation
  - TOCTOU on a security check
  - hardcoded secrets, weak crypto, broken cert validation
  - sensitive data (secrets, PII) in logs or error responses

  LOW VALUE — note briefly, keep looking:
  - null-pointer deref at small fixed offsets with no attacker control
  - assertion failures / clean error returns (correct handling, not a bug)

DO NOT REPORT (common false positives — skip even if technically present):
  - volumetric DoS / rate-limiting / resource-exhaustion — BUT unbounded
    recursion, algorithmic-complexity blowup, or ReDoS driven by untrusted
    input ARE reportable
  - memory-safety findings in memory-safe languages outside unsafe/FFI
  - XSS in React/Angular/Vue unless via dangerouslySetInnerHTML,
    bypassSecurityTrustHtml, v-html, or equivalent raw-HTML escape hatch
  - findings in test files, fixtures, build scripts, docs, or .ipynb
  - missing hardening / best-practice gaps with no concrete exploit
  - env vars and CLI flags as the attack vector (operator-controlled)
  - regex injection, log spoofing, open redirect, missing audit logs
  - outdated third-party dependency versions

{if --extra <file> was given: append its contents here verbatim}

For each finding you DO report, trace: where does the untrusted input
enter, what path reaches the sink, and what condition triggers it.

OUTPUT — one block per finding, nothing else:

<finding>
<id>F-{focus_idx:02d}-{n:02d}</id>
<file>{relative/path}</file>
<line>{line_number}</line>
<category>{heap-buffer-overflow | use-after-free | integer-overflow | sql-injection | command-injection | path-traversal | deserialization | xss | auth-bypass | hardcoded-secret | ...}</category>
<severity>{HIGH | MEDIUM | LOW}</severity>
<confidence>{0.0-1.0}</confidence>
<title>{one line}</title>
<description>{root cause, attacker control, trigger condition, data flow from entry to sink. Cite line numbers.}</description>
<exploit_scenario>{concrete attack: what input, from where, causing what outcome}</exploit_scenario>
<recommendation>{specific fix: parameterize the query, bounds-check before memcpy, etc.}</recommendation>
</finding>

SEVERITY: severity is impact-on-asset, not vuln class. Name the asset the
finding compromises and what it is worth in THIS deployment. HIGH requires
a high-value asset actually present here (secrets, sessions, code
execution, regulated data) — never inferred from the category: XSS
severity depends on what the origin protects (sessions? cookies? actions?);
file-read severity on what the process filesystem actually holds; SSRF
severity on what is reachable and whether the allowlist/DNS is
attacker-influenceable. MEDIUM = real impact under a stated condition
(state it in the description). LOW = reachable but the asset is absent or
low-value here. Severity is not a reporting filter — report reachable
findings at the severity the asset supports.

If you find nothing reportable in your area after a thorough read, emit a
single <finding> with category=none and a one-line note of what you covered.

Step 3 — Collate

  1. Collect <finding> blocks from all subagents. Drop category=none placeholders.
  2. Light dedupe — if two findings cite the same file:line with the same category, keep the one with the longer description and note the duplicate id. (Heavy dedupe is /triage's job; don't over-engineer here.)
  3. Assign stable ids F-001, F-002, ... in (severity desc, file, line) order.

Step 3b — Confidence pass (skip if --no-score)

A cheap second-opinion read that ranks findings by signal quality. Nothing is dropped — this pass calibrates confidence so humans and /triage see high-signal findings first. Spawn one Task subagent per finding in parallel with the brief below. Shallow: re-read and score, not a full reachability trace.

Scoring brief (per finding)

You are giving ONE candidate security finding an independent confidence
score. You are NOT deciding whether to keep it — every finding is kept.
You are deciding how likely it is to survive rigorous triage.

FINDING:
{the full <finding> block}

TARGET: {target_dir} (you may Read/Grep inside it; do NOT execute)

STEP 1 — Re-read the cited code. Open {file} around line {line}. Does the
code actually do what the description claims?

STEP 2 — Check against common false-positive patterns (volumetric DoS,
memory-safe language, test/fixture/doc file, framework auto-escape, env-var
vector, missing-hardening-only, regex/log injection, outdated dep). A match
lowers confidence sharply but does not auto-zero it.

STEP 3 — Score 1-10 that this is a real, actionable vulnerability:
  1-3  likely false positive or noise
  4-5  plausible but speculative
  6-7  credible, needs investigation
  8-10 high confidence, clear pattern

OUTPUT (exactly this, nothing else):
  CONFIDENCE: <1-10>
  REASON: <one line>

Resolve: overwrite each finding's confidence with the score (normalized to 0.0-1.0) and attach confidence_reason. Re-sort findings by (confidence desc, severity desc, file, line) and reassign ids F-001.. in that order. Compute low_confidence_count = findings with confidence < 0.4, for the summary line.

Step 4 — Write output

Write both files to <target-dir>/:

VULN-FINDINGS.json — the /triage ingest shape:

{
  "target": "<target-dir>",
  "scanned_at": "<iso8601>",
  "focus_areas": ["..."],
  "findings": [
    {
      "id": "F-001",
      "file": "relative/path.c",
      "line": 123,
      "category": "heap-buffer-overflow",
      "severity": "HIGH",
      "confidence": 0.9,
      "title": "...",
      "description": "...",
      "exploit_scenario": "...",
      "recommendation": "...",
      "confidence_reason": "..."
    }
  ],
  "summary": {"total": 0, "high": 0, "medium": 0, "low": 0, "low_confidence": 0}
}

Findings are sorted by confidence desc (then severity, file, line), so the top of the file is the highest-signal material.

VULN-FINDINGS.md — human-readable: a summary table (id | severity | category | file:line | title), then one ### F-NNN section per finding with the full description.

Step 5 — Hand back

Tell the user:

  1. Counts: N findings (H/M/L split, X low-confidence), across K focus areas, from M source files.
  2. Top 3 by confidence, one line each.
  3. Next step: > /triage <target-dir>/VULN-FINDINGS.json --repo <target-dir>
  4. Remind: these are static candidates, not verified. For execution-verified crashes, an autonomous harness is needed (see HARNESS.md).

Constraints

  • Never execute target code. No builds, no docker, no network, and no Bash beyond the read-only whitelist in § Tool fallbacks. If the user asks you to "reproduce" or "confirm with a PoC," decline and point at an autonomous execution harness (HARNESS.md).
  • Don't fabricate line numbers. Every file:line you emit must be something you Read or Grep'd. If unsure of the exact line, cite the function and say so in the description.
  • Stay in <target-dir>. Don't follow symlinks or .. out of it.
  • Findings are candidates for /triage, not final verdicts. This skill never drops a finding — Step 3b only ranks. /triage does the rigorous N-vote verification and is where false positives actually get removed.

Provenance

Adapted (Apache-2.0) from anthropics/defending-code-reference-harness — the vuln-scan skill and its autonomous find/recon pipeline prompts. The focus-area recon pattern and memory-safety quality tiers mirror the same logic the autonomous pipeline uses, applied statically. The broader category menu, DO-NOT-REPORT exclusions, per-finding confidence pass, and exploit_scenario/recommendation output fields originate in anthropics/claude-code-security-review's /security-review command. See HARNESS.md for the execution-verified pipeline this static skill complements.

What ships with it: 3 files

12.9 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most security skills give in ~3.4k tokens

Counted across 648 of the 828 authors here whose files we hold, read 2026-08-07

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  • Run dependency audits before every releasein 21 of 648, across 10 files
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  • Copy dependencies before source codein 20 of 648, across 9 files
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Said here and by no other author read

  • spawn one subagent per focus area
  • cap subagents at ten concurrent
  • use single pass on tiny targets
  • report findings with plausible exploit paths
  • drop placeholder findings with category none
  • deduplicate findings with same file and category

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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