---
name: C/C++ Security Review
slug: c-c-security-review
category: Quality
description: C/C++ Security Review performs comprehensive security audits of native C/C++ applications for memory corruption, integer overflows, race conditions, and platform-specific vulnerabilities. Use it when auditing userspace daemons, services, or applications for memory safety issues.
github: "https://github.com/trailofbits/skills/tree/main/plugins/c-review/skills/c-review"
language: Python
stars: 6592
forks: 567
install: "npx degit https://github.com/trailofbits/skills/tree/main/plugins/c-review/skills/c-review ~/.claude/skills/c-review"
installs_to: ~/.claude/skills/c-review
source_path: plugins/c-review/skills/c-review/SKILL.md
collection_size: 25
category_size: 1354
collection_url: "https://dirskills.com/collections/trailofbits/skills"
added: 2026-08-15T06:51:48.936Z
last_synced: 2026-08-15T06:51:48.936Z
canonical_url: "https://dirskills.com/skills/c-c-security-review"
---

# C/C++ Security Review

C/C++ Security Review performs comprehensive security audits of native C/C++ applications for memory corruption, integer overflows, race conditions, and platform-specific vulnerabilities. Use it when auditing userspace daemons, services, or applications for memory safety issues.

**Install:**

```bash
npx degit https://github.com/trailofbits/skills/tree/main/plugins/c-review/skills/c-review ~/.claude/skills/c-review
```

## README

# C/C++ Security Review

Runs in the main conversation (invoke via `/c-review:c-review`). Orchestrator owns the `Task*` ledger as bookkeeping for retries; workers and judges have no Task tools. Workers and judges are named plugin subagents (`c-review:c-review-worker`, `c-review:c-review-dedup-judge`, `c-review:c-review-fp-judge`); tool sets are declared in `plugins/c-review/agents/*.md`. Findings are exchanged via markdown-with-YAML files in a shared output directory.

## When to Use

Native C/C++ application security review: memory safety, integer overflow, races, type confusion, Linux/macOS daemons, Windows userspace services.

## When NOT to Use

- Kernel drivers/modules (Linux, Windows, macOS).
- Managed languages (Java, C#, Python, Go, Rust).
- Embedded/bare-metal code without libc.

## Subagents

| Subagent type | Purpose | Tool set |
|---|---|---|
| `c-review:c-review-worker` | Run assigned cluster, write findings | Read, Write, Edit, Bash |
| `c-review:c-review-dedup-judge` | Merge duplicates (runs **first**) | Read, Write, Edit, Glob |
| `c-review:c-review-fp-judge` | FP + severity + final reports (runs **second**) | Read, Write, Edit, Bash |

Tools come from each agent's frontmatter at spawn time. The orchestrator's `Task*`/`Agent`/`Bash`/etc. come from this skill's `allowed-tools`. **Search-tool / `Bash` interaction:** in current Claude Code, an agent granted `Bash` is **not** also granted the dedicated `Glob` **or `Grep`** tools (the calls return `No such tool available`; the harness expects `find`/`grep`/`rg` via `Bash` instead). So only the dedup-judge — the one agent that holds **no** `Bash` — uses `Glob`; the worker, fp-judge, and the orchestrator resolve and search paths with `Read` / `Bash` `find` / `rg` / `grep` / `test -f` instead. Because the cluster/finder prompt seeds are written in ripgrep regex syntax (`\s`, `\d`, `\b`), `Bash`-holding agents must run them with **`rg`** — a plain `grep -E` may silently mishandle `\s` and return a false-empty (a bad `cleared`). If `rg` is not installed its call fails *loudly* (`command not found`) — fall back to `grep -E` with POSIX classes (`\s`→`[[:space:]]`, drop `\b`), never a raw-`\s` `grep`. Do **not** reintroduce `Glob`/`Grep` into a `Bash`-holding agent's protocol.

---

## Architecture

```
coordinator: write context.md → build_run_plan.py → TaskCreate × M
          → spawn primer (foreground) → spawn M workers (parallel)
          → classify Phase-7 outcomes + write findings-index.txt
          → dedup-judge → fp-judge → report safety net (SARIF + REPORT.md) → return REPORT.md
```

Output directory contains: `context.md`, `plan.json`, `worker-prompts/`, `findings/`, `findings-index.d/` (per-worker shards), `findings-index.txt`, `coverage/` (per-worker coverage-gate files), `run-summary.md`, `dedup-summary.md`, `fp-summary.md`, `REPORT.md`, `REPORT.sarif`.

**Path convention:** every later phase shells out to `${C_REVIEW_PLUGIN_ROOT}/scripts/*.py`, so resolve that variable first to the plugin directory that contains `prompts/clusters/buffer-write-sinks.md` (and `scripts/build_run_plan.py`). Try in order, first hit wins:

1. **Native Claude Code** — `${CLAUDE_PLUGIN_ROOT}`, accepted if `Bash: ls "${CLAUDE_PLUGIN_ROOT}/prompts/clusters/buffer-write-sinks.md"` resolves.
2. **Codex** — `${CODEX_PLUGIN_ROOT}` (set it the same way if that var is present and resolves the marker).
3. **Fallback search** — covers Codex installs under `~/.codex`, Claude installs under `~/.claude`, and a local checkout / repo run: `Bash: find ~/.claude ~/.codex . -path '*/plugins/c-review/prompts/clusters/buffer-write-sinks.md' -print -quit 2>/dev/null`. Take the match and strip the trailing `/prompts/clusters/buffer-write-sinks.md` to get the root (the home dirs are searched before `.` so an installed copy wins over any vendored copy in the audited repo).

Set `C_REVIEW_PLUGIN_ROOT` to the resolved root. If all three fail, **abort** with a message naming the roots searched — do not enter Phase 4 with an empty variable (every `python3 "${C_REVIEW_PLUGIN_ROOT}/scripts/..."` call would fail with a confusing path error).

**Scope convention:** keep two scopes separate throughout the run:

- `finding_scope_root` — the user-requested audit subtree. Workers may only file findings whose vulnerable location is inside this subtree.
- `context_roots` — read-only repo roots/files workers and judges may inspect to verify reachability, callers, wrappers, build flags, mitigations, and threat-model details. Default to `.` unless the user explicitly forbids broader context. Reading context outside `finding_scope_root` is allowed; filing findings there is not.

---

## Rationalizations to Reject

- **"Background spawns parallelize the workers."** They do not — `Agent` calls in a single assistant message already run concurrently. `run_in_background=true` defeats the Phase 6a primer cache, so every worker pays full cache-creation (`cache_read_input_tokens=0`) and the ~15 K-token primer is wasted M times. This is the single most common defect — multiple recent runs spawned 7-of-8 (or all) workers with `bg=true`. Default: omit `run_in_background` from worker spawns.
- **"I'll re-derive the cluster list / paths / pass prefixes inline instead of running `build_run_plan.py`."** The script is the only authority for selection and rendering. Paraphrasing it drops fields that the worker self-check requires, producing `worker-N abort: spawn prompt malformed`. Always run the script and `Read plan.json`.
- **"The run partially succeeded — I'll just write `REPORT.md` from what completed."** Hiding partial runs behind a successful report is a correctness bug. If any Phase-5 cluster task is not `completed`, surface it prominently in `run-summary.md` and the final response.
- **"Zero findings — skip Phase 8."** Always run both judges and Phase 8b: dedup-judge writes a minimal no-op `dedup-summary.md` on an empty index, fp-judge writes empty `REPORT.md`/`REPORT.sarif`, and Phase 8b's SARIF generator emits `results: []` for the empty case. SARIF consumers depend on a stable artifact set.
- **"`Bash: ls README*` is fine for the preflight."** Under zsh, an unmatched glob aborts the whole compound command before `2>/dev/null` runs. Use `find` (never fails on no-match) — and not `Glob`, which is unavailable to an agent that also holds `Bash`.

---

## Orchestration Workflow

Run these phases **in the main conversation**.

### Phase 0: Parameter Collection

**Entry:** skill invoked. **Exit:** `threat_model`, `worker_model`, `severity_filter` resolved; `scope_subpath` resolved or set to `"."`; `finding_scope_root=scope_subpath`; `context_roots` resolved.

The skill is invoked directly (no command wrapper). Parse any free-text arguments the user passed on the `/c-review:c-review` line (e.g. `flamenco only`, `high severity only`, `use haiku`) and pre-fill the answers they imply — then ask for any missing required parameters with **one** `AskUserQuestion` call. Never silently default the required parameters.

Required parameters:

| Parameter | Values | How to infer from args |
|---|---|---|
| `threat_model` | `REMOTE` / `LOCAL_UNPRIVILEGED` / `BOTH` | Words like "remote", "network", "attacker" → `REMOTE`; "local", "unprivileged" → `LOCAL_UNPRIVILEGED`; otherwise ask. |
| `worker_model` | `haiku` / `sonnet` / `opus` | Explicit model name in args. Otherwise ask (no silent default). |
| `severity_filter` | `all` / `medium` / `high` | "all", "every", "noisy" → `all`; "medium and above" → `medium`; "high only", "criticals only" → `high`. Otherwise ask — **no silent default**. |
| `scope_subpath` | repo-relative directory (optional) | Phrases like "X only", "just audit X/", "review subdirectory X" → `src/X/` or the matching subdir. Apply fuzzy matching against top-level subdirectories of the repo. If absent, set `"."`; if ambiguous, ask. |

Call `AskUserQuestion` exactly once with only unresolved required parameters (`threat_model`, `worker_model`, `severity_filter`) plus `scope_subpath` only when the user explicitly requested a narrowed scope but it is ambiguous. If the required parameters were all pre-filled and scope is absent or resolved, skip the question.

After resolving `scope_subpath`, set `finding_scope_root="${scope_subpath:-.}"`. Set `context_roots="."` by default so workers can verify callers/build settings outside a narrowed subtree without filing out-of-scope findings. If the user explicitly asks to forbid broader context, set `context_roots="${finding_scope_root}"` and note that reachability confidence may be lower.

### Phase 1: Prerequisites

**Entry:** Phase 0 complete. **Exit:** `is_cpp`, `is_posix`, `is_windows` flags determined.

Probe within `${finding_scope_root:-.}` with the `Bash` commands below (non-empty output ⇒ flag true). The dedicated `Grep`/`Glob` tools are unavailable to this orchestrator because it holds `Bash` — use `grep`/`rg`/`find` via `Bash`. (If a probe regex uses `\s`/`\b` and your `grep` lacks GNU `\s` support, run it with `rg -uu` — which honors `\s` and still searches ignored files — or replace `\s`→`[[:space:]]` and drop `\b`. Widening is safe: a false-positive flag only adds a harmless worker, a missed match would skip a pass.):

```bash
# is_cpp
find "${finding_scope_root:-.}" -type f \( -name '*.cpp' -o -name '*.cxx' -o -name '*.cc' -o -name '*.hpp' -o -name '*.hh' \) -print -quit
# is_posix
grep -rlE '#include[[:space:]]*<(pthread|signal|sys/(socket|stat|types|wait)|unistd|errno)\.h>' \
  --include='*.c' --include='*.h' \
  --include='*.cpp' --include='*.cxx' --include='*.cc' --include='*.hpp' --include='*.hh' \
  "${finding_scope_root:-.}" | head -1
# is_windows
grep -rlE '#include[[:space:]]*<(windows|winbase|winnt|winuser|winsock|ntdef|ntstatus)\.h>' \
  --include='*.c' --include='*.h' \
  --include='*.cpp' --include='*.cxx' --include='*.cc' --include='*.hpp' --include='*.hh' \
  "${finding_scope_root:-.}" | head -1
```

`compile_commands.json` is informational (no agent currently uses LSP), but the probe is mandatory so the run summary records whether richer local tooling is available. Probe via `Glob: **/compile_commands.json` under `${context_roots}`. If `Glob` is unavailable, use:

```bash
printf '%s\n' "${context_roots:-.}" | tr ',' '\n' | while IFS= read -r root; do
  [ -n "$root" ] && find "$root" -name compile_commands.json -print -quit
done | head -1
# `find "$root"` is quoted intentionally so a context root containing spaces
# (e.g. "/Users/me/My Repo") survives word-splitting. Do not unquote it.
```

If absent, suggest CMake `-DCMAKE_EXPORT_COMPILE_COMMANDS=ON`/Bear/compiledb to the user but continue.

### Phase 2: Output Directory

**Entry:** Phase 1 flags set. **Exit:** absolute `output_dir` resolved; `${output_dir}/findings/` and `${output_dir}/coverage/` exist.

Resolve an absolute path for `output_dir` (default: `$(pwd)/.c-review-results/$(date -u +%Y%m%dT%H%M%SZ)/`):

```bash
mkdir -p "${output_dir}/findings" "${output_dir}/coverage"
```

The `coverage/` subdirectory holds per-worker coverage-gate audit files (`coverage/worker-{N}.md`). Workers write to it instead of embedding the table in their reply — see `agents/c-review-worker.md` step 5.

### Phase 3: Codebase Context

**Entry:** `${output_dir}` exists. **Exit:** `${output_dir}/context.md` written.

Skim `README.{md,rst,txt}` and any build file (`Makefile`, `CMakeLists.txt`, `meson.build`, `configure.ac`) — preflight with `find` (via `Bash`) before any `Read` (a `Read` on a missing file aborts the turn; `Glob` is unavailable to this orchestrator because it holds `Bash`). Do **not** use `Bash: ls README*` for the preflight: under zsh, an unmatched glob aborts the whole compound command before `2>/dev/null` runs (observed: a Phase-3 `ls src/X/README*` call failed with `no matches found` and dropped the entire preflight). Use `find . -maxdepth 2 -name 'README*' -o -name 'Makefile' -o -name 'CMakeLists.txt' -o -name 'meson.build'`, which never fails on no-match.

Write `${output_dir}/context.md` with: YAML frontmatter (`threat_model`, `severity_filter`, `scope_subpath`, `finding_scope_root`, `context_roots`, `is_cpp`, `is_posix`, `is_windows`, `output_dir`, `compile_commands` as `present`/`absent` plus path when present), then a short markdown body with five sections — **Purpose** (1-3 sentences), **Scope** (what's in `finding_scope_root`, and that findings outside it are out of scope), **Entry points** (where untrusted data enters: network, files, CLI, IPC), **Trust boundaries** (sandboxed vs trusted peers vs arbitrary remote), **Existing hardening** (fuzzing corpora, sanitizers, privilege separation).

### Phase 4: Build Run Plan (deterministic)

**Entry:** language flags + `threat_model` known; `${output_dir}/findings/` exists. **Exit:** `${output_dir}/plan.json` and `${output_dir}/worker-prompts/*.txt` written; `M = worker_count` known.

Selection, filtering, path resolution, and spawn-prompt rendering are **delegated to the script** to prevent the "orchestrator paraphrases the spawn template and drops fields" failure mode:

```bash
python3 "${C_REVIEW_PLUGIN_ROOT}/scripts/build_run_plan.py" \
  --plugin-root "${C_REVIEW_PLUGIN_ROOT}" --output-dir "${output_dir}" \
  --threat-model "${threat_model}" --severity-filter "${severity_filter}" \
  --scope-subpath "${finding_scope_root:-.}" --context-roots "${context_roots:-.}" \
  --is-cpp "${is_cpp}" --is-posix "${is_posix}" --is-windows "${is_windows}" \
  --max-passes-per-worker 4
```

The script writes `plan.json` + `worker-prompts/worker-N.txt` + (if `--cache-primer=true`, the default) `worker-prompts/cache-primer.txt`, and prints a JSON summary on stdout. Exits non-zero on any missing prompt — surface the message and stop. Typical M with the default `--max-passes-per-worker 4` (REMOTE, `is_posix=true`): 13 (C POSIX), 15 (C++ POSIX), 16 (C POSIX + Windows), 18 (C++ POSIX + Windows); `LOCAL_UNPRIVILEGED` adds ~1 because `ambient-state` keeps its two REMOTE-skipped passes. After it returns, `Read plan.json` for the structured selection — never re-derive filtering or paths.

`--max-passes-per-worker N` caps the per-worker pass count. The planner deterministically splits any **non-consolidated** cluster with more than `N` passes into `ceil(K/N)` contiguous chunks; each chunk becomes its own `c-review-worker` spawn with a `-{i}`-suffixed `cluster_id` (e.g. `arithmetic-type-1`, `arithmetic-type-2`). **The consolidated cluster `buffer-write-sinks` (13 passes) is exempt — never chunked, regardless of pass count or override — so one worker builds its shared Phase-A inventory once and runs every phase** (chunking a consolidated cluster would force each chunk to rebuild that inventory, which workers skip in practice). The shared prompt-cache prefix and `Cluster prompt:` path are byte-identical across chunks, so the cache primer still warms every worker. Default 4 is calibrated against the heavy non-consolidated clusters in `manifest.json`. Some output-heavy non-consolidated clusters may declare a smaller manifest-level `max_passes_per_worker` override so each expensive pass group gets a smaller worker. Pass `--max-passes-per-worker 0` to disable all chunking, including manifest overrides (one worker per cluster). Because `buffer-write-sinks` runs all 13 passes in one worker, it is the heaviest worker in the fan-out; if it ever hits the soft tool-call cap on a large codebase, surface its truncation note rather than splitting it (splitting re-introduces the inventory-skip).

### Phase 5: Create Bookkeeping Tasks (orchestrator-internal)

**Entry:** `${output_dir}/plan.json` exists; `M = plan.workers.length`. **Exit:** `cluster_task_ids[]` created (1:1 with `plan.workers`), all `pending`.

The task ledger is **orchestrator bookkeeping only** (TUI visibility + Phase-7 retry tracking) — workers never read or write it. One `TaskCreate` per worker, populating `metadata` with `kind="cluster"`, `worker_n`, `cluster_id`, `spawn_prompt_path`, `pass_prefixes`, `attempt=1` — all values copied verbatim from `plan.workers[i]`. Track `cluster_task_ids[]` in `plan.workers` order.

### Phase 6: Spawn workers (optional cache-primer first, then M in parallel)

**Entry:** `cluster_task_ids[]` populated; per-worker spawn prompt files exist at `${output_dir}/worker-prompts/worker-N.txt`. **Exit:** all M `Agent` calls — across every wave — have returned (the parallel spawn block(s) completed).

#### Phase 6a: Cache primer (gated on `plan.run.cache_primer`)

A parallel batch from cold start cannot share cache (all M requests dispatch simultaneously, none has finished writing). To warm the prefix, spawn a tiny primer first — **foreground** (background spawns don't share cache with subsequent foreground spawns).

If `plan.run.cache_primer == true`, `build_run_plan.py` has written `${output_dir}/worker-prompts/cache-primer.txt`. Spawn it in its own assistant message: `Read` the file, pass verbatim as `Agent` `prompt` with `subagent_type=c-review:c-review-worker`, `model=${worker_model}`, `description="C review cache primer"`, no `run_in_background`. The script wrote the prefix byte-identical to `worker-1.txt` through the `<context>` block — that byte-identity is what gives the parallel workers their cache hit. The primer trailer contains `Cache primer: true`, which the worker system prompt treats as a first-class mode and returns exactly `worker-PRIMER abort: cache primer (no analysis performed)` in one text response with zero tool calls. Discard the abort line — Phase 7 ignores it (no `worker-N` id).

Foreground spawn already serializes — no `sleep` needed before Phase 6b. Skip Phase 6a entirely if `plan.run.cache_primer == false`.

#### Phase 6b: Spawn M real workers in parallel (one message per wave of ≤16)

> **STOP — read this before composing the spawn message.**
>
> Workers MUST be spawned **foreground** (no `run_in_background` field, or `run_in_background=false`).
> "Parallel" here means *one assistant message containing the wave's `Agent` calls* — that already runs them concurrently. (For large `M`, split into consecutive waves of ≤16 calls, one message per wave — see "Required spawn shape" below.) **Background spawns are NOT how you parallelize this skill.**
>
> Background spawns defeat Phase 6a's primer cache: every worker pays full cache-creation on its first turn (`cache_read_input_tokens=0`), and the primer's ~15 K tokens are wasted M times over. Two real runs (audit logs available) had exactly this symptom — every worker started with `first_cr=0`.
>
> Before sending the spawn message, audit your draft: every `Agent` call must have **no** `run_in_background` key. If you wrote `run_in_background=true`, delete it.

**Required spawn shape:** emit a single assistant message containing the wave's `Agent` tool invocations — that one message is what runs them concurrently. Sequential spawning (one `Agent` call per message) serializes the review and is also wrong, but that failure is loud (timing); the background-spawn failure is silent (cost).

**Waves when `M` exceeds the per-message cap.** The harness caps the number of `Agent` calls it will dispatch from a single assistant message (observed: ~20 in Claude Code — a real 25-worker run silently kept only the first 20 and had to spawn the rest in a second message). So when `M > 16`, **plan the waves up front**: split the workers into consecutive waves of **≤16 `Agent` calls**, each wave its own single assistant message. Rules:

- **Within a wave:** all `Agent` calls in **one** message, **foreground** (no `run_in_background`) — identical shape to a single-wave run.
- **Across waves:** wave _k+1_ is a **separate** message that can only be sent after wave _k_'s `Agent` calls all return (a tool-use message ends the turn). Waves are therefore serialized with respect to each other — that is correct and loud; accept it.
- **Never** reach for `run_in_background=true` to fit more workers in one message. More *waves*, never background — backgrou