yaramint 0.1.7

Generate YARA rules automatically from positive and negative examples. For PII detection, secret scanning, prompt injection, and any pattern-based detection use case.

YaraMint

YARA rules from examples, not hand-crafting

YaraMint generates YARA rules from labeled data. Provide a set of adversarial samples and a benign control corpus. It then mines statistically discriminative n-gram patterns, scores them against false positive rate on the control set, and writes the surviving signatures as a standard .yar file. Full algorithm writeup here.

Use Cases

Secret and API key detection — Train on known key formats with benign code as the control set. Get a rule tuned to your specific patterns with minimal false positives.

PII detection in data pipelines — Custom PII formats vary by industry and organization. Generic regex rule sets do not cover internal ID schemes, regional document formats, or domain-specific identifiers. YaraMint learns them from your own examples.

Prompt injection and jailbreak detection — Generate rules from known attack datasets and validate against benign prompt corpora before deploying to your RAG pipeline or agent infrastructure.

Threat hunting and malware analysis — Given samples from an incident, mint hunting rules to scan your fleet for variants. The positive/negative framing maps directly to the analyst workflow.

Supply chain and compliance scanning — Detect license-incompatible snippets, known vulnerable code patterns, or banned dependencies across large codebases in CI.

Installation

Requires Python 3.13 or higher.

pip install yaramint

Using uv (recommended):

uv pip install yaramint

Getting Started

This example generates a rule set for detecting leaked API keys, using a corpus of benign source code as the control set.

Step 1 — Prepare your benign corpus

If your benign dataset is large, prepare it once and reuse it across rule generations:

ymint prepare ./data/source_code_corpus.jsonl \
  --adapter jsonl \
  --output ./data/benign_code.jsonl

Step 2 — Generate rules

Point yaramint at your positive examples (known API key formats) and the prepared benign control set:

ymint generate ./data/api_keys.jsonl \
  --adversarial-adapter jsonl \
  --benign-dataset ./data/benign_code.jsonl \
  --benign-adapter jsonl \
  --output ./data/api_key_rules.yar

Step 3 — Deploy

The output is a standard .yar file. Load it into any YARA engine, your CI pipeline, a pre-commit hook, or a SIEM. No additional runtime required:

yara ./data/api_key_rules.yar ./target_directory/

Optional — Find the best configuration

Run a grid search to find optimal hyperparameters for your dataset before generating production rules:

ymint optimize ./data/api_keys.jsonl \
  --benign-dataset ./data/benign_code.jsonl \
  --config optimization_config.yaml

The optimizer prints a ready-to-use ymint generate command with the best flags applied.

Commands

ymint prepare

Preprocesses a large benign dataset for efficient reuse. Run once, reference in every subsequent generate call. Accepts local files or Hugging Face datasets:

ymint prepare bigcode/the-stack-smol \
  --adapter huggingface \
  --output ./data/benign_code.jsonl

ymint generate

The main command. Mines discriminative patterns from your adversarial examples, validates them against the benign control set, and writes a YARA rule file:

ymint generate ./data/pii_examples.jsonl \
  --adversarial-adapter jsonl \
  --benign-dataset ./data/benign_text.jsonl \
  --benign-adapter jsonl \
  --engine ngram \
  --output ./data/pii_rules.yar

Tune sensitivity with the --set flag:

ymint generate ./data/pii_examples.jsonl \
  --benign-dataset ./data/benign_text.jsonl \
  --set engine.score_threshold=0.9 \
  --output ./data/pii_rules.yar

Iterating on existing rules? Skip patterns already covered:

ymint generate ./data/new_samples.jsonl \
  --benign-dataset ./data/benign_text.jsonl \
  --existing-rules ./data/baseline.yar \
  --output ./data/updated_rules.yar

ymint optimize

Runs a hyperparameter grid search and outputs the best ymint generate command for your dataset. Use this before generating production rules on a new dataset:

ymint optimize ./data/samples.jsonl \
  --benign-dataset ./data/benign_text.jsonl \
  --config optimization_config.yaml

Output and Compatibility

yaramint produces standard .yar files that:

• Work with any YARA-compatible engine
• Integrate natively with VirusTotal, most SIEMs, EDRs, osquery, and Velociraptor
• Are human-readable, auditable, and version-controllable like any other code
• Require no proprietary runtime to deploy

Further Reading

• User Guide — full configuration reference, adapter options, dot-notation overrides, and engine tuning
• Algorithm and design — how the pattern mining engine works