/vector-forge

name: vector-forge
description: "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."

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:

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