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Vector Forge

Trail of BitsOfficialPublished by Trail of Bits

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From the author

Mutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.
— the description by Trail of Bits

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Vector Forge provides a systematic methodology to improve cryptographic test suites by using mutation testing to identify coverage gaps. It guides users through discovering implementations, establishing a baseline, performing graph-informed escape analysis, and generating targeted test vectors. By comparing mutation kill rates before and after, it provides concrete evidence of test suite effectiveness, ensuring that both valid and invalid inputs are rigorously handled.

What it does

  • Identifies code paths missed by existing test suites using mutation testing.
  • Uses call-graph analysis to prioritize escaped mutants by security impact.
  • Generates targeted test vectors to close coverage gaps and kill surviving mutants.
  • Validates test vector effectiveness through before-and-after mutation kill rate comparisons.
  • Supports cross-implementation verification to ensure consistency across different codebases.
  • Provides a structured reporting template for documenting test suite improvements.

When to use it

  • When generating or improving test vectors for cryptographic algorithms.
  • When evaluating the concrete coverage of an existing test suite.
  • When identifying implementation code paths that lack test coverage.
  • When creating cross-implementation test suites for cryptographic primitives.

Advantages

  • Systematically uncovers blind spots in cryptographic test suites.
  • Prioritizes security-critical code paths for testing.
  • Provides measurable proof of test suite improvement.
  • Reduces reliance on manual analysis for identifying missing test cases.
  • Ensures both positive and negative test assertions are implemented.

Usage examples

“How can I improve the test coverage for my BLS12-381 implementation?”
The agent will guide you through running a baseline mutation test, using the call graph to identify escaped mutants, and generating specific test vectors to target those uncovered paths.
“My cryptographic library has high line coverage but still misses bugs. What should I do?”
The agent will recommend using Vector Forge to perform mutation testing, which reveals logic gaps that standard line coverage metrics fail to detect.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/trailofbits/vector-forge/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip vector-forge.zip -d .agents/skills

Documentation by Trail of Bits

Vector Forge

Uses mutation testing to systematically identify gaps in test vector coverage, then generates new test vectors that close those gaps. Measures effectiveness by comparing mutation kill rates before and after.

When to Use

  • Generating test vectors for cryptographic algorithms or protocols

  • Evaluating how well existing test vectors cover an implementation

  • Finding implementation code paths that no test vector exercises

  • Creating Wycheproof-style cross-implementation test vectors

  • Measuring the concrete coverage value of a test vector suite

When NOT to Use

  • No implementations exist yet (need code to mutate)

  • Single trivial implementation with no edge cases

  • Testing application logic rather than algorithm implementations

  • The algorithm has no public test vectors to compare against

Prerequisites

  • trailmark installed — if uv run trailmark fails, run:

    uv tool install trailmark

Python snippets: uv run --with trailmark python - (a tool env is not importable)

- At least one implementation of the target algorithm in a
language with mutation testing support
- A test harness that consumes test vectors and exercises
the implementation
- A mutation testing framework for the target language

---

## Rationalizations to Reject

| Rationalization | Why It's Wrong | Required Action |
|-----------------|----------------|-----------------|
| "We have enough test vectors" | Mutation testing proves otherwise | Run the baseline first |
| "The implementation's own tests are sufficient" | Own tests often share blind spots with the impl | Cross-impl vectors catch different bugs |
| "FFI crates can be mutation tested at the binding layer" | Mutations to wrappers don't affect the underlying impl | Mutate the actual implementation language |
| "Timeouts mean the mutation was caught" | Timeouts are ambiguous — could be killed or alive | Resolve timeouts before drawing conclusions |
| "All mutants are equivalent" | Most aren't — verify by reading the mutation | Classify each escaped mutant individually |
| "Checking valid vectors is enough" | Permissive mutations survive without negative assertions | Assert rejection for every invalid vector |
| "Manual analysis is fine" | Manual analysis misses what tooling catches | Install and run the tools |

---

## Workflow Overview

Phase 1: Discovery → Find implementations to test ↓ Phase 2: Harness → Write/adapt test vector harness for each impl ↓ Phase 3: Baseline → Run mutation testing with existing vectors ↓ Phase 4: Escape Analysis → Classify escaped mutants by code path ↓ Phase 5: Vector Gen → Create test vectors targeting escapes ↓ Phase 6: Validation → Re-run mutation testing, compare before/after ↓ Output: Coverage Report + New Test Vectors


---

## Phase 1: Discovery

Find implementations of the target algorithm. Look for:

1. **Pure implementations** in high-level languages (Go, Rust, Python)
   — these are the best mutation testing targets
2. **FFI wrapper crates** — identify these early so you don't waste
   time mutating wrapper glue code
3. **Reference implementations** — useful for cross-verification but
   may not be the best mutation targets

For each implementation, note:
- Language and mutation testing framework
- Whether it's pure code or FFI wrappers
- Existing test suite size and coverage
- Which API surface the test vectors will exercise

### Implementation Type Classification

| Type | Mutation Value | Example |
|------|---------------|---------|
| Pure implementation | High | zkcrypto/bls12_381 (Rust), gnark-crypto (Go) |
| FFI bindings to C/asm | Low at binding layer | blst Rust crate |
| C/C++ implementation | High (use Mull) | blst C library |
| Generated code | Medium (mutations may be equivalent) | gnark-crypto generated field arithmetic |

**Key insight:** If an implementation delegates to another language
via FFI, you must mutate the *underlying* implementation, not the
bindings. For C/C++ underneath Rust/Go/Python, use Mull or similar.

---

## Phase 2: Harness

For each implementation, create a test harness that:

1. Reads test vectors from JSON files (Wycheproof format recommended)
2. Exercises the implementation's API for each vector
3. Asserts **both acceptance and rejection**:
   - Valid vectors: deserialization succeeds, output matches expected
   - Invalid vectors: deserialization fails or verification rejects
4. Adds **roundtrip assertions** for valid deserialization vectors:
   `serialize(deserialize(bytes)) == bytes`
5. Reports pass/fail per vector with test IDs

**Critical:** A harness that only checks valid vectors will miss all
permissive mutations (e.g., `&` → `|` in validation). See
[references/lessons-learned.md](references/lessons-learned.md) §7.

The harness must be runnable by the mutation testing framework.
For most frameworks this means:
- **Go:** A `_test.go` file in the same package as the implementation
- **Rust:** An integration test in `tests/` or inline `#[test]` functions
- **Python:** A pytest test file
- **C/C++:** A test binary linked against the implementation

### Harness Placement

The harness must live *inside the implementation's package* so the
mutation framework can see it. This usually means:

```bash
# Go: add test file to the package being mutated
cp wycheproof_test.go /path/to/impl/package/

# Rust: add integration test
cp wycheproof.rs /path/to/crate/tests/

# Python: add test to the test directory
cp test_wycheproof.py /path/to/package/tests/
Handling Existing Vectors

If the implementation already has test vectors:

  1. Run mutation testing with ONLY the existing vectors (baseline)

  2. Run mutation testing with ONLY your new vectors

  3. Run mutation testing with BOTH combined

  4. The delta between (1) and (3) shows the new vectors' value


Phase 3: Baseline

Run mutation testing with existing test vectors only.

Framework Selection

See references/mutation-frameworks.md for language-specific setup.

LanguageFrameworkCommand
Gogremlinsgremlins unleash ./path/to/package
Rustcargo-mutantscargo mutants -j N --timeout T
Pythonmutmutmutmut run --paths-to-mutate src/
C/C++Mullmull-runner -test-framework=GoogleTest binary
Parallelism

Always use parallel execution for large codebases:

  • cargo mutants -j 8 (Rust, 8 parallel workers)

  • gremlins unleash --timeout-coefficient 3 (Go, increase timeouts)

  • mutmut run --runner "pytest -x -q" (Python, fail-fast)

Recording Baseline Results

Capture these metrics per implementation:

MetricDescription
Total mutantsNumber of mutations generated
KilledMutants caught by tests
Survived/LivedMutants NOT caught (these are the targets)
Not coveredCode paths no test reaches at all
Timed outAmbiguous — resolve before comparing
Efficacy %Killed / (Killed + Survived)
Coverage %(Total - Not covered) / Total

Save the full mutation log for Phase 4 analysis.


Phase 4: Escape Analysis (Graph-Informed Triage)

Classify each escaped (survived + not covered) mutant using the Trailmark call graph for reachability and blast radius analysis.

This phase MUST use the genotoxic skill's triage methodology. The call graph transforms mutation results from a flat list of survived mutants into an actionable, prioritized set of vector targets.

Step 1: Build the Call Graph

Build a Trailmark code graph for each implementation before triaging mutations:

# Go
uv run trailmark analyze --language go --summary {targetDir}

# Rust
uv run trailmark analyze --language rust --summary {targetDir}

The graph provides:

  • Caller chains — trace from public API entry points to mutated functions to determine reachability

  • Cyclomatic complexity — prioritize high-CC functions

  • Blast radius — functions with many callers have wider impact if their mutations survive

Step 2: Filter to Relevant Code

Mutation frameworks test the entire package. Filter results to only the files/functions that test vectors should exercise:

# Go (gremlins)
grep -E "(LIVED|NOT COVERED)" baseline.log \
  | grep -E " at (relevant|files)" \
  | sort

# Rust (cargo-mutants)
cat mutants.out/missed.txt | grep "src/relevant"
Step 3: Graph-Informed Classification

For each escaped mutant, map it to its containing function in the call graph and apply the genotoxic triage criteria:

Graph SignalClassificationAction
No callers in graphFalse PositiveDead code, skip
Only test callersFalse PositiveTest infrastructure
Logging/display/formattingFalse PositiveCosmetic
Cross-package callers but NOT COVEREDCross-Package GapSee below
Reachable from public API, low CCMissing VectorDesign targeted vector
Reachable from public API, high CC (>10)Fuzzing TargetBoth vector + fuzz harness
Validation/error-handling pathNegative VectorCraft invalid input that triggers path
Optimization path (GLV, SIMD, batch)Edge-Case VectorInput that triggers optimization threshold
|→^ after left shift (e.g. (t<<1) | carry)Equivalent MutantSkip — bit 0 always 0, OR=XOR
ct_eq &→| on Montgomery limbsAPI-UnreachableNeeds library-internal tests, not vectors
Equivalent mutation (behavior unchanged)False PositiveSkip
Step 4: Identify Cross-Package Test Gaps

Critical pitfall: Mutation frameworks often only run tests within the same package as the mutation. For Go (gremlins) and Rust (cargo-mutants), this means:

  • A mutation in hash_to_curve/g2.go only runs tests in the hash_to_curve package, NOT tests in the parent bls12381 package that imports it

  • Functions that are fully exercised by cross-package tests will appear as NOT COVERED — these are false positives

  • To confirm: check if the mutated function is called from a test in a different package that wouldn't be run

To resolve cross-package gaps:

  1. Add a thin test in the sub-package that calls through the same code path as the cross-package test

  2. Or run gremlins with --test-pkg ./... (if supported)

  3. Or document as a framework limitation in the report

Step 5: Prioritize by Security Impact

Using the call graph, rank surviving mutants by impact:

PriorityCriteriaExample
P0 — CriticalMutant weakens validation/equality/authenticationct_eq: & → | makes equality permissive
P1 — HighMutant in deserialization flag parsingfrom_compressed: & → | accepts invalid flags
P2 — MediumMutant in field arithmetic internalsFp::square: | → ^ corrupts computation
P3 — LowMutant in optimization pathphi endomorphism: only affects performance path
SkipFormatting, display, equivalent mutationDebug::fmt return value replacement
Step 6: Group by Vector Strategy

Group escaped mutants by the code path they represent and the type of test vector needed:

Deserialization flag validation (P1):
  - g1.rs:339,363-365,384 — from_compressed_unchecked flags
  → Need: valid-point-wrong-flag vectors

Field arithmetic (P2):
  - fp.rs:371-376,406,635-643 — subtract_p, neg, square
  → Need: field arithmetic KATs with edge-case values

Optimization thresholds (P3):
  - g1.go:68, g2.go:75 — GLV vs windowed multiplication
  → Need: scalar multiplication with large scalars

Cross-package (framework limitation):
  - hash_to_curve/g2.go:242-278 — isogeny, sgn0
  → Document as false positive or add sub-package test

Each group becomes a target for new test vectors in Phase 5.


Phase 5: Vector Generation

For each escaped code path group, design test vectors that force execution through that path.

Vector Design Patterns
Code Path TypeVector Strategy
Point deserializationMalformed points: wrong length, invalid field elements, off-curve, wrong subgroup, identity point
Signature verificationValid sig + all single-bit corruptions of sig, pk, msg
Hash-to-curveKnown answer tests (KATs) with edge-case inputs: empty, single byte, max length
Aggregate operations1 signer, many signers, duplicate signers, mixed valid/invalid
Error handlingEvery error path should have a vector that triggers it
Arithmetic edge casesZero, one, field modulus - 1, points at infinity
Serialization flagsEvery valid flag combination + every invalid flag combination
Roundtrip integrityFor every valid deser vector, assert serialize(deserialize(b)) == b
Carry/reduction faultsReimplement at reduced limb widths, inject faults, extract distinguishing inputs
Single-Fault Negative Vectors

Each negative vector should have exactly one defect with everything else valid — this isolates which validation check is being tested. See references/vector-patterns.md for per-flag construction examples.

Fault Simulation (Limb-Width Reimplementation)

When mutation testing only applies local operator swaps, deeper architectural bugs (carry propagation, reduction overflow) go untested. To close this gap, reimplement the target algorithm at reduced limb widths (8, 16, 25, 32 bits) and deliberately inject faults — then generate vectors that catch them.

See references/fault-simulation.md for the full methodology: limb-width selection, fault injection catalog, vector extraction, and validation workflow.

Cross-Implementation Verification

Every new test vector MUST be verified against at least two independent implementations before being added to the suite:

  1. Generate the vector using implementation A

  2. Verify with implementation B (different codebase, ideally different language)

  3. If B disagrees, investigate — one implementation has a bug

Vector Format

Use Wycheproof JSON format (algorithm, testGroups[].tests[] with tcId, comment, result, flags). See references/vector-patterns.md for the full schema.

Wycheproof contributions: Use Wycheproof's vectorgen tool rather than formatting vector files directly. Supply the generated changes as an envelope. The vectorgen tool can add, update, or replace vectors while handling tcId assignment, test counts, canonical formatting, and schema validation. Go-based generators can avoid the vectorgen CLI tool and instead call the programmatic github.com/c2sp/wycheproof/vectorgen API.

See references/lessons-learned.md §14 and the upstream vectorgen guide for the current workflow and commands.


Phase 6: Validation

Re-run mutation testing with the new test vectors included.

Tip: Use per-file mutation testing for fast iteration during vector development (see references/lessons-learned.md §12). Only run full-crate tests for the final comparison.

Before/After Comparison
MetricBaselineWith New VectorsDelta
KilledXYY - X
SurvivedABA - B (should decrease)
Not CoveredCDC - D (should decrease)
Efficacy %E%F%F - E
Success Criteria

Vectors have both retroactive value (killing mutants in existing code) and proactive value (catching bugs in future implementations). Generate both kinds — boundary-condition vectors may not improve kill rates in mature libraries but will catch bugs in new implementations. See references/lessons-learned.md §13.

Retroactive (measurable): previously survived/uncovered mutants become killed, no regressions.

If kill rates don't change: the implementation's own tests likely already cover those paths. The vectors still add cross-implementation verification value. Document which case applies.


Output Format

Write VECTOR_FORGE_REPORT.md covering: target algorithm, implementations tested, baseline results, escape analysis, new vectors generated, after results, before/after delta, and conclusions. See references/report-template.md for the full template.


Quality Checklist

Before delivering:

  • [ ] At least one pure implementation mutation-tested (not just FFI wrappers)

  • [ ] Baseline run completed with existing vectors

  • [ ] Trailmark call graph built for each implementation

  • [ ] All escaped mutants triaged using graph-informed classification

  • [ ] Cross-package false positives identified and documented

  • [ ] Security-critical mutations (ct_eq, validation, auth) prioritized as P0/P1

  • [ ] Fault simulation and mutation-derived vectors cross-verified against 2+ implementations

  • [ ] After run completed with new vectors included

  • [ ] Before/after delta computed and explained

  • [ ] Report written to VECTOR_FORGE_REPORT.md

  • [ ] New test vectors saved in standard format (Wycheproof JSON)


Integration

SkillRelationship
genotoxic (required for Phase 4)Provides graph-informed triage — call graph cuts actionable mutants by 30-50%
mutation-testing (mewt/muton)Use for Solidity; Vector Forge is language-agnostic
property-based-testingBetter than hand-crafted vectors for bitwise mutations in field arithmetic
testing-handbook-skills (fuzzing)Functions with CC > 10 and surviving mutants need both vectors and fuzz harnesses

Supporting Documentation

Files

references/mutation-frameworks.md

Mutation Testing Frameworks

Language-specific setup, execution, and output parsing for mutation testing.

Contents

  • Language detection

  • Framework reference table

  • Per-language setup and commands

  • Parsing survived mutants

  • Necessist (test statement removal)


Installation Policy

Every mutation testing framework listed below MUST be installed before proceeding. If a framework command is not found or fails to install:

  1. Try the primary install method for the platform

  2. Try the alternative install methods listed in the language section

  3. If all methods fail, report the error to the user — do NOT fall back to "manual mutation analysis", "manual verification", or any other substitute that skips running the tool

Manual analysis is not a replacement for mutation testing. Mutation testing tools systematically apply hundreds or thousands of mutations that manual review cannot replicate. Skipping installation and doing manual analysis produces false confidence with minimal actual coverage.


Language Detection

Use file extensions to determine the target language, then select the appropriate mutation framework:

ExtensionsLanguageFramework
.pyPythonpytest-gremlins or mutmut
.js, .jsx, .ts, .tsxJavaScript/TypeScriptStryker
.rsRustcargo-mutants
.goGogremlins or go-mutesting
.javaJavaPITest
.c, .h, .cpp, .hpp, .ccC/C++Mull
.csC#Stryker.NET
.rbRubymutant
.phpPHPInfection
.hsHaskellMuCheck or Hedgehog

Python: pytest-gremlins (preferred) or mutmut

pytest-gremlins

Faster alternative to mutmut. Uses mutation switching (no file I/O or module reloads), coverage-guided test selection, and parallel execution. Requires Python 3.11+.

Install:

uv add --dev pytest-gremlins

Run:

uv run pytest --gremlins

No configuration needed — it integrates directly with pytest.

Parse survived mutants: pytest-gremlins reports survived gremlins in its test output. Each entry includes the file, line, mutation type, and original/replacement values.

mutmut

Install:

uv add --dev mutmut

Configure in pyproject.toml:

[tool.mutmut]
paths_to_mutate = "src/"
tests_dir = "tests/"
runner = "python -m pytest -x -q"

Run:

uv run mutmut run
uv run mutmut results

Parse survived mutants:

# List survived mutant IDs
uv run mutmut results | grep "Survived"

# Show specific mutant
uv run mutmut show <id>

# Export all results as JSON (mutmut 3.x+)
uv run mutmut junitxml > mutmut-results.xml

Extract from results output: Each survived mutant line contains the file path, line number, and mutation description. Parse with:

uv run mutmut results 2>&1 | grep "Survived" | \
  sed 's/.*Survived: //'

macOS note: If using rustworkx or other Rust extensions, set:

export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES

JavaScript/TypeScript: Stryker

Install:

pnpm add -D @stryker-mutator/core
pnpm dlx stryker init

Configure stryker.config.json:

{
  "mutate": ["src/**/*.ts", "!src/**/*.test.ts"],
  "testRunner": "vitest",
  "reporters": ["json", "clear-text"],
  "jsonReporter": { "fileName": "stryker-report.json" }
}

Run:

pnpm dlx stryker run

Parse survived mutants:

# JSON report at reports/mutation/stryker-report.json
# Filter survived:
cat reports/mutation/stryker-report.json | \
  jq '.files | to_entries[] | .value.mutants[] | select(.status == "Survived")'

Output fields: mutatorName, replacement, location.start.line, location.start.column, fileName.


Rust: cargo-mutants

Install:

cargo install cargo-mutants

Run:

cargo mutants --json

Parse survived mutants:

# Results in mutants.out/outcomes.json
cat mutants.out/outcomes.json | \
  jq '.[] | select(.outcome == "survived")'

Output fields: scenario.function, scenario.file, scenario.line, scenario.replacement, outcome.

Filtering by module:

cargo mutants --file src/parser.rs --json

Go: gremlins (preferred) or go-mutesting

gremlins

Actively maintained mutation testing tool for Go. Works best on small-to-medium Go modules (microservices, libraries).

Install:

# macOS
brew tap go-gremlins/tap && brew install gremlins

# Any platform with Go
go install github.com/go-gremlins/gremlins/cmd/gremlins@latest

Run:

gremlins unleash .

Parse results: gremlins reports survived mutants to stdout with file path, line number, and mutation type.

go-mutesting

Install:

go install github.com/zimmski/go-mutesting/cmd/go-mutesting@latest

Run:

go-mutesting ./...

Parse results: go-mutesting prints survived mutants to stdout. Each line contains the file, line number, and mutation operator.

Alternative: native fuzzing (Go 1.18+)
go test -fuzz=FuzzTarget -fuzztime=60s ./pkg/...

Java: PITest

Configure in pom.xml:

<plugin>
  <groupId>org.pitest</groupId>
  <artifactId>pitest-maven</artifactId>
  <configuration>
    <targetClasses>com.example.*</targetClasses>
    <outputFormats>XML,CSV</outputFormats>
  </configuration>
</plugin>

Run:

mvn org.pitest:pitest-maven:mutationCoverage

Parse survived mutants:

# Results in target/pit-reports/mutations.xml
# Filter SURVIVED status
grep 'status="SURVIVED"' target/pit-reports/*/mutations.xml

Output fields: mutatedClass, mutatedMethod, lineNumber, mutator, status.


C/C++: Mull

Mull is an LLVM-based mutation testing tool for C and C++. It works as a compiler plugin — it instruments the compiled test binary with mutations, then selectively activates them during test execution.

Mull requires a specific LLVM version. Check the Mull releases page for the LLVM version supported by the latest release. The project must compile with the matching Clang version.

Install

Mull is distributed as prebuilt binaries on GitHub Releases. Each binary targets a specific LLVM version — you must match the Mull binary's LLVM version to the Clang version installed on the system.

Step 1: Determine your Clang/LLVM version:

clang --version
# Look for the major version number (e.g., 19, 20)

If Clang is not installed, install it first. On macOS, use brew install llvm@<version>. On Ubuntu, use sudo apt-get install clang-<version>.

Step 2: Download the matching Mull binary.

Go to the Mull releases page and download the asset matching your LLVM version, platform, and architecture. Asset naming convention:

Mull-<LLVM_MAJOR>-<MULL_VERSION>-LLVM-<LLVM_FULL>-<OS>-<ARCH>.<ext>

Examples (Mull 0.29.0):

PlatformLLVMAsset
macOS arm6419Mull-19-0.29.0-LLVM-19.1.7-macOS-aarch64-*.zip
macOS arm6420Mull-20-0.29.0-LLVM-20.1.8-macOS-aarch64-*.zip
Ubuntu 24.04 amd6419Mull-19-0.29.0-LLVM-19.1.1-ubuntu-amd64-24.04.deb
Ubuntu 24.04 amd6420Mull-20-0.29.0-LLVM-20.1.2-ubuntu-amd64-24.04.deb
RHEL 9 amd6420Mull-20-0.29.0-LLVM-20.1.8-rhel-amd64-9.6.rpm

Step 3: Install.

macOS:

# 1. Install the matching LLVM/Clang version via Homebrew
#    Check Mull releases for which LLVM versions are available
brew install llvm@18  # or llvm@19, llvm@20

# 2. Download the matching Mull binary
gh release download --repo mull-project/mull \
  --pattern 'Mull-18-*-macOS-aarch64-*.zip'  # match LLVM version
unzip Mull-18-*.zip

# 3. Install binaries to a known location
sudo mkdir -p /usr/local/bin /usr/local/lib
sudo cp usr/local/bin/mull-runner-* /usr/local/bin/
sudo cp usr/local/bin/mull-reporter-* /usr/local/bin/
sudo cp usr/local/lib/mull-ir-frontend-* /usr/local/lib/

# 4. Verify
mull-runner-18 --version
/opt/homebrew/opt/llvm@18/bin/clang --version

Important macOS notes:

  • The Mull binary's LLVM version must exactly match the installed Clang. Using brew install llvm@18 with Mull-19-* will not work.

  • Use the Homebrew Clang, not Apple's system Clang (which is a different LLVM version and lacks plugin support).

  • Set ulimit -n 1024 before running mull-runner (see Environment Setup section below).

Ubuntu/Debian:

# Option A: Cloudsmith APT repository
curl -1sLf \
  'https://dl.cloudsmith.io/public/mull-project/mull-stable/setup.deb.sh' \
  | sudo -E bash
sudo apt-get update
sudo apt-get install mull-19  # match your LLVM version

# Option B: Direct .deb from GitHub
gh release download --repo mull-project/mull \
  --pattern 'Mull-19-*-ubuntu-amd64-24.04.deb'
sudo dpkg -i Mull-19-*.deb

RHEL/Fedora:

# Option A: Cloudsmith RPM repository
curl -1sLf \
  'https://dl.cloudsmith.io/public/mull-project/mull-stable/setup.rpm.sh' \
  | sudo -E bash
sudo dnf install mull-20  # match your LLVM version

# Option B: Direct .rpm from GitHub
gh release download --repo mull-project/mull \
  --pattern 'Mull-20-*-rhel-amd64-*.rpm'
sudo rpm -i Mull-20-*.rpm

Verify installation:

mull-runner --version

If mull-runner is not found after installation, check that the install prefix is on $PATH. DO NOT fall back to "manual mutation analysis" — fix the installation or report the error.

Configure and Build

Mull requires the project to be compiled with Clang and the Mull compiler plugin. The plugin injects mutations at the LLVM IR level.

Key build requirements:

  • Use the same Clang version that matches your Mull release

  • Pass -fpass-plugin=<path-to-mull-ir-frontend> to the compiler

  • Use -g -O0 (debug info required, no optimization)

  • Disable assembly (--disable-asm) — Mull can only mutate LLVM IR, not hand-written assembly

  • Disable hardening flags that interfere: --disable-ssp --disable-pie

Find the plugin path:

# The plugin is typically installed alongside mull-runner:
# Linux:  /usr/lib/mull-ir-frontend-<N>  (or mull-ir-frontend.so)
# macOS:  <install-prefix>/lib/mull-ir-frontend-<N>
# Use `find` or `locate` if unsure:
find /usr/local /opt/homebrew /tmp -name "mull-ir-frontend*" 2>/dev/null

Simple projects:

MULL_PLUGIN=$(find /usr/local /opt/homebrew -name "mull-ir-frontend*" 2>/dev/null | head -1)
clang -fpass-plugin=$MULL_PLUGIN -g -O0 \
  -o test_binary test_main.c src/*.c

Autotools projects (configure/make):

MULL_PLUGIN=$(find /usr/local /opt/homebrew -name "mull-ir-frontend*" 2>/dev/null | head -1)
LLVM_BIN=$(dirname $(which clang))  # or /opt/homebrew/opt/llvm@18/bin

CC=$LLVM_BIN/clang \
CFLAGS="-fpass-plugin=$MULL_PLUGIN -g -grecord-command-line -O0" \
./configure --disable-shared --enable-static --disable-asm \
  --disable-ssp --disable-pie
make clean && make -j$(nproc)

CMake projects:

set(CMAKE_C_COMPILER clang)
set(CMAKE_CXX_COMPILER clang++)
set(MULL_PLUGIN_PATH "" CACHE STRING "Path to Mull plugin")
if(MULL_PLUGIN_PATH)
  add_compile_options(-fpass-plugin=${MULL_PLUGIN_PATH} -g -O0)
endif()
MULL_PLUGIN=$(find /usr/local /opt/homebrew -name "mull-ir-frontend*" 2>/dev/null | head -1)
cmake -B build -DMULL_PLUGIN_PATH=$MULL_PLUGIN
cmake --build build
Run
# Set FD limit (required on macOS, see Environment Setup)
ulimit -n 1024

# Run with GoogleTest binary
mull-runner --allow-surviving --no-output --timeout=5000 \
  --reporters=Elements --report-dir=mull-report ./build/tests

# Run with custom test command
mull-runner --test-program=ctest ./build/tests

# Generate report
mull-runner --report-dir=mull-report ./build/tests

Recommended flags:

  • --allow-surviving — don't treat survived mutants as errors

  • --no-output — suppress stdout/stderr from mutant runs

  • --timeout=5000 — 5 second timeout per mutant (adjust based on baseline test runtime; use 1000ms for tests completing in <100ms)

  • --reporters=Elements — JSON output in Mutation Testing Elements format (machine-parseable for triage)

  • --report-dir=DIR — write JSON reports to this directory

  • --report-name=NAME — control output filename (useful when running multiple test binaries)

  • --workers=N — parallelism for mutant execution (defaults to CPU count)

Parse survived mutants

Mull outputs results to stdout and optionally to report files. Each survived mutant includes the file path, line number, and mutation type.

# JSON report (if --report-dir used)
cat mull-report/mutation-testing-report.json | \
  jq '.files | to_entries[] | .value.mutants[] |
    select(.status == "Survived")'
Environment Setup (Required)

Before running mull-runner, always set a bounded file descriptor limit. On macOS (especially Tahoe / macOS 26+), the default ulimit -n is unlimited, which causes Mull's subprocess library (reproc) to fail with EINVAL when it tries to close inherited file descriptors in the forked child process. The fix:

# REQUIRED before any mull-runner invocation
ulimit -n 1024

Add this to your Mull runner scripts or shell session. Without it, you will see:

[error] Cannot run executable: Invalid argument

Root cause: reproc calls getrlimit(RLIMIT_NOFILE) to determine the max FD to close. When the soft limit is RLIM_INFINITY, reproc computes max_fd = INT_MAX, which exceeds its internal MAX_FD_LIMIT (1048576) safety check, causing the child to exit with EMFILE.

Troubleshooting
ProblemSolution
mull-runner: command not foundInstall Mull using the instructions above
Cannot run executable: Invalid argumentRun ulimit -n 1024 before mull-runner (see Environment Setup above)
LLVM version mismatchInstall the LLVM version matching your Mull release
Plugin load errorRecompile with matching Clang version
No mutants generatedEnsure -g -O0 flags and Mull plugin are active
Tests fail without mutationsFix test suite first — Mull needs a green baseline
Original test failed (timeout)Increase --timeout or skip tests with long baseline runtimes

C#: Stryker.NET

Install:

dotnet tool install -g dotnet-stryker

Run:

dotnet stryker --reporter json

Parse survived mutants:

cat StrykerOutput/*/reports/mutation-report.json | \
  jq '.files | to_entries[] | .value.mutants[] | select(.status == "Survived")'

Ruby: mutant

Install:

gem install mutant

Run:

bundle exec mutant run --include lib --require mylib 'MyLib*'

Parse results: mutant outputs surviving mutations to stdout with file paths, line numbers, and mutation descriptions.


PHP: Infection

Install:

composer require --dev infection/infection

Run:

vendor/bin/infection --show-mutations --min-msi=0

Parse survived mutants:

# JSON log at infection-log.json
cat infection-log.json | jq '.survived[]'

Haskell: MuCheck or Hedgehog

MuCheck is the primary mutation testing tool for Haskell. For projects without MuCheck support, property-based testing with Hedgehog or QuickCheck serves as a mutation-resistant alternative.

MuCheck

Install:

cabal install MuCheck

Run:

mucheck -t "cabal test" src/MyModule.hs

MuCheck applies standard mutation operators (negate guards, swap operators, replace patterns) to the target module and runs the test suite against each mutant.

Parse survived mutants: MuCheck prints results to stdout. Each survived mutant includes the file path, line number, and mutation description (e.g., "Negated guard on line 42").

Limitations: MuCheck requires the project to build with cabal and has limited support for large multi-module projects. For Stack-based projects, wrap the test command: mucheck -t "stack test" src/Module.hs.

Alternative: property-based testing as mutation proxy

For projects where MuCheck is impractical, strong property-based tests provide equivalent mutation resistance. Properties that assert invariants over all inputs catch most mutations that MuCheck would surface.

Hedgehog (preferred):

cabal install hedgehog

Write properties in test/ that cover arithmetic, branching, and boundary behavior. A comprehensive property suite catches the same classes of defects as mutation testing.

QuickCheck:

cabal install QuickCheck

QuickCheck properties work similarly. Use forAll with custom generators to target the input domain of each function under test.


Universal Mutant Record Format

Regardless of framework, normalize each survived mutant to this schema before feeding into Phase 3 triage:

{
  "file_path": "src/parser.py",
  "line": 42,
  "mutation_type": "arithmetic_operator",
  "original": "+",
  "replacement": "-",
  "function_name": "parse_header",
  "status": "survived"
}

Map the containing function name by matching file_path:line against trailmark graph nodes using their location.start_line and location.end_line ranges.


Necessist: Test Statement Removal

Necessist complements mutation testing by removing statements and method calls from test code and re-running the tests. If a test still passes after a statement is removed, that statement may be unnecessary — indicating weak assertions or missing coverage.

Mutation testing mutates production code to check if tests detect changes. Necessist mutates test code to check if each test statement is actually needed. Run both when the language supports it.

Supported Frameworks
FrameworkLanguageAuto-detected
AnchorRust (Solana)Yes
FoundrySolidityYes
GoGoYes
Hardhat (TypeScript)TypeScriptYes
RustRustYes
VitestJavaScript/TypeScriptYes

Necessist auto-detects the framework from project files. Use --framework to override when auto-detection fails.

Install
cargo install necessist
Run
# Auto-detect framework, run on all test files
necessist

# Explicit framework selection
necessist --framework foundry

# Target specific test files
necessist tests/test_parser.rs tests/test_validator.rs

# Set timeout per test (default 60s, 0 = no timeout)
necessist --timeout 120

# Resume a previous run (results stored in SQLite)
necessist --resume
Parse Results

Necessist stores results in a SQLite database by default. Use --dump to export:

necessist --dump

Each result line contains the test file, line number, the removed statement, and whether the test passed or failed after removal. Filter to passed after removal entries — these are the findings to triage.

Configuration

Create necessist.toml in the project root (necessist --default-config generates a template):

ignored_functions = ["println", "eprintln", "dbg"]
ignored_methods = ["clone", "to_string", "unwrap"]
ignored_macros = ["debug_assert", "trace"]
  • ignored_functions — Skip removals of these function calls

  • ignored_methods — Skip removals of these method calls

  • ignored_macros — Skip removals of these macro invocations

For Foundry projects, consider ignoring common cheatcodes that are setup-only (e.g., vm.label, vm.deal for labeling/funding).

Normalized Necessist Record Format

Normalize each finding before feeding into Phase 3 triage:

{
  "test_file_path": "tests/test_parser.rs",
  "test_line": 42,
  "removed_statement": "parser.validate(&input)",
  "test_function": "test_parse_header",
  "status": "passed_after_removal",
  "source": "necessist"
}

The source field distinguishes necessist findings from mutation testing results during triage and reporting. Map the removed statement to a production function using the graph analysis algorithm described in the genotoxic skill.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Trail of Bits
Publisher
Trail of Bits (Official)
Source
trailofbits/skills · plugins/trailmark/skills/vector-forge · commit e4dd0bb
Licence
Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Trail of Bits or Trail of Bits.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download vector-forge.zip