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 pip install trailmark
  

• 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

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

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.,

&

|

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:

# 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.

gremlins unleash ./path/to/package

cargo mutants -j N --timeout T

mutmut run --paths-to-mutate src/

mull-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:

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:

|

^

(t<<1) | carry

&

|

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:

ct_eq

&

|

from_compressed

&

|

Fp::square

|

^

phi

Debug::fmt

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

serialize(deserialize(b)) == b

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[]

tcId

comment

result

flags

JSON encoding: Wycheproof canonicalizes vectors with

reformat_json.py

• Go: Use

  json.NewEncoder

  +

  enc.SetEscapeHTML(false)

  — never

  json.Marshal

  /

  json.MarshalIndent

  , which silently escape

  >

  →

  \u003e

  ,

  <

  →

  \u003c

  ,

  &

  →

  \u0026

• Python:

  json.dumps

  is safe by default
• Node.js:

  JSON.stringify

  is safe by default

See references/lessons-learned.md §14 for details.

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

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

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

Supporting Documentation

• references/mutation-frameworks.md - Language-specific mutation testing framework setup
• references/vector-patterns.md - Common test vector patterns for cryptographic primitives
• references/fault-simulation.md - Limb-width reimplementation for carry, reduction, and overflow faults
• references/report-template.md - Full markdown template for the Vector Forge report
• references/lessons-learned.md - BLS12-381 case study: FFI kill rates, timeout masking, cross-package false positives, bitwise mutation gaps, and security-critical priorities