{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-garak","slug":"pypi-garak","name":"garak","type":"cli","url":"https://pypi.org/project/garak/","page_url":"https://unfragile.ai/pypi-garak","categories":["testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-garak__cap_0","uri":"capability://safety.moderation.multi.model.vulnerability.scanning.with.pluggable.harnesses","name":"multi-model vulnerability scanning with pluggable harnesses","description":"Garak scans LLMs for vulnerabilities by routing prompts through a modular harness system that abstracts different model providers (OpenAI, Anthropic, Ollama, vLLM, etc.) behind a unified interface. Each harness handles authentication, rate limiting, and response parsing for its target model, allowing the same vulnerability test suite to run against any LLM without code changes. The architecture uses a plugin-based loader pattern to dynamically instantiate harnesses at runtime based on configuration.","intents":["Test an LLM API endpoint for jailbreak vulnerabilities without writing provider-specific code","Compare vulnerability exposure across multiple models by running identical test suites","Integrate LLM security scanning into CI/CD pipelines with minimal setup","Audit proprietary or self-hosted LLMs using the same framework as public APIs"],"best_for":["security teams evaluating LLM deployment risk","LLM providers building internal red-teaming infrastructure","enterprises auditing third-party LLM integrations"],"limitations":["Harness coverage limited to explicitly implemented providers — custom models require writing new harness code","Rate limiting and quota handling delegated to harness implementations — inconsistent behavior across providers","No built-in cost tracking — high-volume scanning against paid APIs can incur unexpected charges","Synchronous harness execution creates bottlenecks when scanning many models sequentially"],"requires":["Python 3.8+","Valid API credentials for target LLM provider (OpenAI key, Anthropic key, etc.)","Network access to LLM endpoints","Sufficient API quota/rate limits for test volume"],"input_types":["model provider identifier (string)","model name/ID (string)","API credentials (environment variables or config file)","optional: custom prompt templates (text)"],"output_types":["structured vulnerability report (JSON/CSV)","model response logs (text)","aggregated risk metrics (numeric)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_1","uri":"capability://safety.moderation.probe.based.vulnerability.test.generation.and.execution","name":"probe-based vulnerability test generation and execution","description":"Garak organizes vulnerability tests as 'probes' — modular test units that generate adversarial prompts, send them to a target LLM via a harness, and evaluate responses against detection criteria. Probes are organized into taxonomies (e.g., 'jailbreak', 'prompt-injection', 'hallucination') and can be composed into test suites. Each probe implements a generate() method that produces test prompts (often using templates or programmatic construction) and a detect() method that classifies model responses as vulnerable or safe based on heuristics, keyword matching, or semantic similarity.","intents":["Run a curated set of jailbreak tests against an LLM to identify exploitation vectors","Create custom vulnerability probes for domain-specific attack patterns","Automate detection of unsafe model behaviors (refusals bypass, harmful content generation)","Track vulnerability trends across model versions or fine-tuning iterations"],"best_for":["red teamers building custom attack test suites","LLM safety researchers evaluating mitigation strategies","compliance teams documenting LLM risk assessments"],"limitations":["Detection heuristics are often rule-based (keyword/regex matching) — brittle against paraphrased or obfuscated responses","Probe coverage is manually curated — emerging attack patterns require new probe implementations","No adaptive testing — probes don't learn from model responses to refine subsequent tests","Semantic detection relies on external models (e.g., embeddings) — adds latency and cost"],"requires":["Python 3.8+","Garak framework installed","Target LLM harness configured and authenticated","Optional: embedding model for semantic similarity detection"],"input_types":["probe taxonomy/name (string)","probe configuration (JSON/YAML)","optional: custom prompt templates (text)"],"output_types":["vulnerability detection results (boolean/confidence score)","model response text (string)","probe execution logs (structured data)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_10","uri":"capability://automation.workflow.cli.and.programmatic.api.for.test.execution","name":"cli and programmatic api for test execution","description":"Garak exposes both a command-line interface (CLI) and a Python API for executing vulnerability scans. The CLI uses argparse to parse configuration and invoke the orchestrator, making garak accessible to non-programmers. The Python API provides classes and functions for programmatic test execution, enabling integration into Python-based workflows, notebooks, and CI/CD pipelines. Both interfaces share the same underlying orchestrator, ensuring consistent behavior. The architecture uses a facade pattern to abstract CLI and API differences, allowing users to choose the interface that best fits their workflow.","intents":["Run garak scans from the command line without writing code","Integrate garak into Python-based CI/CD pipelines or automation scripts","Use garak in Jupyter notebooks for interactive vulnerability exploration","Build custom tools or dashboards that invoke garak programmatically"],"best_for":["security teams using garak in CI/CD pipelines","researchers using garak in Python notebooks","DevOps engineers automating LLM security gates","non-technical users running garak from the command line"],"limitations":["CLI argument parsing is complex — steep learning curve for new users","Python API documentation is minimal — requires reading source code to understand usage","No interactive mode — users must pre-define all configuration before running","Error messages are sometimes cryptic — difficult to debug configuration issues"],"requires":["Python 3.8+","Garak framework installed","Bash shell (for CLI) or Python environment (for API)"],"input_types":["CLI arguments (strings) or Python objects (dicts, classes)","configuration files (YAML/JSON)","model credentials (environment variables)"],"output_types":["test results (JSON/CSV/HTML)","execution logs (text)","exit codes (integer)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_2","uri":"capability://automation.workflow.configurable.test.suite.orchestration.and.reporting","name":"configurable test suite orchestration and reporting","description":"Garak provides a configuration-driven orchestration layer that chains together harnesses, probes, and detectors into executable test suites. Users define test runs in YAML/JSON config files specifying which models to test, which probes to run, and how to aggregate results. The orchestrator handles sequential or parallel probe execution (depending on harness concurrency support), collects results, and generates structured reports (JSON, CSV, HTML) with vulnerability metrics, model comparisons, and risk summaries. The architecture uses a run manager pattern to track test state and enable resumable/incremental scanning.","intents":["Define a repeatable security test suite for an LLM and run it on a schedule","Generate compliance-ready vulnerability reports comparing multiple models","Integrate LLM scanning into CI/CD pipelines with minimal scripting","Resume interrupted scans without re-running completed probes"],"best_for":["DevSecOps teams automating LLM security gates","compliance officers documenting LLM risk assessments","LLM platform teams running periodic red-team audits"],"limitations":["Configuration schema is complex — steep learning curve for non-technical users","Reporting templates are fixed — custom report formats require template modification","No built-in alerting — requires external monitoring to act on vulnerability results","Run resumption relies on local state files — distributed scanning requires custom state management"],"requires":["Python 3.8+","Garak framework installed","YAML/JSON config file defining test suite","All target harnesses configured and authenticated"],"input_types":["test suite configuration (YAML/JSON)","model credentials (environment variables)","optional: custom report templates (Jinja2)"],"output_types":["vulnerability report (JSON/CSV/HTML)","test execution logs (structured data)","risk metrics and aggregations (numeric)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_3","uri":"capability://text.generation.language.adversarial.prompt.generation.with.template.and.programmatic.strategies","name":"adversarial prompt generation with template and programmatic strategies","description":"Garak's probes generate adversarial prompts using multiple strategies: template-based (filling placeholders in predefined jailbreak/injection patterns), programmatic (constructing prompts via Python logic to vary parameters), and potentially LLM-based (using auxiliary models to generate novel attack prompts). Probes can combine strategies — e.g., a jailbreak probe might use templates for known attacks and programmatic generation for variations. The generation layer abstracts prompt construction, allowing probes to focus on detection logic and enabling reuse of generation strategies across multiple probes.","intents":["Generate diverse jailbreak prompts to test LLM robustness without manual prompt engineering","Create parameterized prompt variations (e.g., different injection points, obfuscation techniques)","Extend garak with custom prompt generation logic for domain-specific attacks","Benchmark LLM vulnerability across prompt variations to identify weak points"],"best_for":["red teamers exploring LLM attack surface systematically","researchers studying jailbreak generalization across models","security teams building domain-specific vulnerability tests"],"limitations":["Template-based generation is limited to predefined patterns — novel attacks require new templates","Programmatic generation requires Python coding — not accessible to non-technical users","No built-in prompt diversity metrics — difficult to assess coverage of attack space","LLM-based generation (if used) introduces cost and latency; results may be inconsistent"],"requires":["Python 3.8+","Garak framework installed","Optional: auxiliary LLM for prompt generation (requires additional API credentials)"],"input_types":["probe configuration (JSON/YAML)","optional: custom prompt templates (text)","optional: generation parameters (dict)"],"output_types":["generated adversarial prompts (list of strings)","prompt metadata (e.g., attack type, parameters used)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_4","uri":"capability://safety.moderation.response.evaluation.and.vulnerability.detection.with.multiple.criteria","name":"response evaluation and vulnerability detection with multiple criteria","description":"Garak's detection layer evaluates LLM responses against multiple criteria to classify them as vulnerable or safe. Detection strategies include keyword/regex matching (e.g., detecting refusal phrases or harmful content keywords), semantic similarity (comparing responses to known vulnerable outputs using embeddings), classifier-based detection (using auxiliary ML models to score response safety), and custom heuristics. Probes compose these strategies — e.g., a jailbreak probe might use keyword matching for obvious bypasses and semantic similarity for subtle ones. The detection layer is decoupled from prompt generation, allowing the same response to be evaluated by multiple detectors.","intents":["Automatically classify LLM responses as vulnerable or safe without manual review","Detect subtle jailbreaks that evade simple keyword matching","Combine multiple detection signals to reduce false positives/negatives","Implement custom detection logic for domain-specific vulnerabilities"],"best_for":["security teams automating vulnerability assessment at scale","researchers studying LLM safety evaluation metrics","compliance teams needing reproducible, auditable detection logic"],"limitations":["Keyword-based detection is brittle — easily evaded by paraphrasing or obfuscation","Semantic detection requires embedding models — adds latency (~100-500ms per response) and cost","Classifier-based detection requires training data — not available for all vulnerability types","No ground truth labels — difficult to validate detection accuracy without manual review","False positive/negative rates vary widely across detection strategies — requires tuning per model/domain"],"requires":["Python 3.8+","Garak framework installed","Optional: embedding model for semantic detection (e.g., OpenAI embeddings, local model)","Optional: safety classifier model (e.g., Perspective API, custom model)"],"input_types":["LLM response text (string)","probe configuration specifying detection criteria (JSON/YAML)","optional: reference vulnerable outputs for similarity comparison (list of strings)"],"output_types":["vulnerability classification (boolean or confidence score)","detection signal breakdown (dict with scores per strategy)","explanation/evidence for classification (string)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_5","uri":"capability://safety.moderation.taxonomy.based.vulnerability.classification.and.organization","name":"taxonomy-based vulnerability classification and organization","description":"Garak organizes vulnerabilities into a hierarchical taxonomy (e.g., 'jailbreak', 'prompt-injection', 'hallucination', 'bias', 'privacy') with subtypes and specific probes for each category. The taxonomy is exposed as a discoverable API — users can list available probes, filter by vulnerability type, and understand the coverage of each category. The taxonomy structure enables organized reporting (grouping results by vulnerability class) and helps users understand which attack vectors are tested. The architecture uses a registry pattern to dynamically load probes and organize them by taxonomy.","intents":["Understand what types of vulnerabilities garak can test for","Filter and run only probes for specific vulnerability classes","Generate reports organized by vulnerability taxonomy for stakeholder communication","Identify gaps in vulnerability coverage for a specific LLM"],"best_for":["security teams planning comprehensive LLM audits","compliance officers documenting vulnerability assessment scope","researchers studying LLM vulnerability distributions"],"limitations":["Taxonomy is fixed and curated by garak maintainers — custom vulnerability types require framework modification","Taxonomy coverage is incomplete — emerging vulnerabilities may not have dedicated categories","No quantitative coverage metrics — difficult to assess how thoroughly each category is tested","Taxonomy organization may not align with domain-specific threat models"],"requires":["Python 3.8+","Garak framework installed"],"input_types":["optional: vulnerability type filter (string)"],"output_types":["taxonomy structure (nested dict/JSON)","list of available probes with metadata (structured data)","probe descriptions and coverage information (text)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_6","uri":"capability://automation.workflow.batch.scanning.and.result.aggregation.across.multiple.models","name":"batch scanning and result aggregation across multiple models","description":"Garak supports scanning multiple LLMs in a single test run, aggregating results across models to enable comparative analysis. The orchestrator manages harness instances for each model, routes probes to all harnesses, and collects results in a unified format. Aggregation includes per-model vulnerability counts, cross-model comparisons (e.g., 'Model A is vulnerable to X, Model B is not'), and overall risk rankings. The architecture uses a result collector pattern to normalize outputs from different harnesses and enable flexible aggregation strategies.","intents":["Compare vulnerability exposure across multiple LLM providers in a single test run","Identify which models are most robust to specific attack types","Generate comparative risk reports for model selection decisions","Track vulnerability trends across model versions or fine-tuning iterations"],"best_for":["teams evaluating multiple LLM options for production deployment","LLM providers benchmarking safety against competitors","researchers studying vulnerability distributions across models"],"limitations":["Scanning multiple models sequentially is slow — no built-in parallelization across harnesses","Cost scales linearly with number of models and probes — can be expensive for large-scale comparisons","Aggregation metrics are fixed — custom comparison logic requires report template modification","Results are only comparable if all models are scanned with identical probes — partial scans complicate analysis"],"requires":["Python 3.8+","Garak framework installed","Valid credentials for all target LLM providers","Sufficient API quota/rate limits for all models"],"input_types":["list of model identifiers (list of strings)","test suite configuration (YAML/JSON)","model credentials (environment variables)"],"output_types":["per-model vulnerability results (dict)","cross-model comparison report (JSON/CSV/HTML)","aggregated risk metrics (numeric)"],"categories":["automation-workflow","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_7","uri":"capability://tool.use.integration.extensible.harness.framework.for.custom.llm.integration","name":"extensible harness framework for custom llm integration","description":"Garak provides a harness base class that developers can subclass to add support for new LLM providers or custom deployments. A harness implements methods for authentication, prompt submission, response retrieval, and error handling. The framework handles harness discovery and instantiation via a plugin loader, allowing new harnesses to be added without modifying core garak code. Harnesses can implement provider-specific optimizations (e.g., batch API calls, streaming responses, custom retry logic) while maintaining a uniform interface for the orchestrator. The architecture uses dependency injection to pass configuration to harnesses at runtime.","intents":["Add support for a proprietary or self-hosted LLM to garak's scanning framework","Implement provider-specific optimizations (batching, streaming, custom auth) without modifying core code","Integrate garak with internal LLM platforms or fine-tuned models","Build custom harnesses for research or testing purposes"],"best_for":["enterprises with proprietary LLM deployments","researchers building custom LLM evaluation frameworks","LLM platform teams integrating garak into internal tools"],"limitations":["Harness development requires Python coding — not accessible to non-technical users","No harness testing framework — developers must manually test new harnesses","Harness interface is not versioned — breaking changes to base class affect all custom harnesses","Documentation for harness development is minimal — steep learning curve"],"requires":["Python 3.8+","Garak framework installed","Python development knowledge","Understanding of target LLM's API or interface"],"input_types":["harness base class (Python class)","target LLM credentials/endpoint (string)","optional: custom configuration (dict)"],"output_types":["custom harness implementation (Python class)","LLM responses (string)","error/status information (structured data)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_8","uri":"capability://safety.moderation.probe.extensibility.and.custom.vulnerability.test.development","name":"probe extensibility and custom vulnerability test development","description":"Garak provides a probe base class that developers can subclass to implement custom vulnerability tests. A probe implements generate() (to produce test prompts) and detect() (to evaluate responses) methods. The framework handles probe discovery, instantiation, and execution via a plugin loader. Custom probes can implement domain-specific attacks, novel detection strategies, or variations of existing probes. The architecture uses a probe registry to organize probes by taxonomy and enable dynamic filtering/selection. Probes can depend on external resources (templates, models, APIs) injected at runtime.","intents":["Create custom vulnerability probes for domain-specific attack patterns","Implement novel detection strategies for emerging vulnerabilities","Extend garak's probe taxonomy with organization-specific tests","Develop research probes for studying new LLM vulnerabilities"],"best_for":["security researchers developing novel LLM attack techniques","enterprises building domain-specific vulnerability tests","red teamers creating custom test suites for specific threat models"],"limitations":["Probe development requires Python coding and understanding of garak's architecture","No probe testing framework — developers must manually validate new probes","Probe interface is not versioned — breaking changes affect custom probes","Documentation for probe development is minimal","Probes are tightly coupled to garak's execution model — difficult to reuse in other frameworks"],"requires":["Python 3.8+","Garak framework installed","Python development knowledge","Understanding of target vulnerability type"],"input_types":["probe base class (Python class)","probe configuration (JSON/YAML)","optional: external resources (templates, models, APIs)"],"output_types":["custom probe implementation (Python class)","generated prompts (list of strings)","detection results (boolean/confidence score)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-garak__cap_9","uri":"capability://automation.workflow.result.persistence.and.historical.tracking","name":"result persistence and historical tracking","description":"Garak can persist test results to local files (JSON, CSV) or external databases, enabling historical tracking of vulnerability trends across test runs. The result storage layer abstracts persistence details, allowing results to be written to multiple backends. Users can query historical results to track vulnerability remediation, model improvement, or regression detection. The architecture uses a result writer pattern to normalize outputs from different harnesses and enable flexible storage strategies. Results include metadata (timestamp, model version, probe version) to enable accurate historical comparison.","intents":["Track vulnerability trends across model versions to detect regressions","Document vulnerability remediation efforts for compliance reporting","Compare current scan results against historical baselines","Build dashboards showing LLM security posture over time"],"best_for":["compliance teams documenting LLM security assessments","LLM platform teams monitoring safety metrics over time","security teams tracking vulnerability remediation progress"],"limitations":["No built-in database support — results must be persisted to files or custom backends","No query API — historical analysis requires external tools (SQL, pandas, etc.)","Result schema is fixed — custom metadata requires framework modification","No built-in data retention policies — storage can grow unbounded","Results are not encrypted — sensitive vulnerability data stored in plaintext"],"requires":["Python 3.8+","Garak framework installed","Optional: external database for result storage","Sufficient disk space for result files"],"input_types":["test results (structured data)","result storage configuration (JSON/YAML)","optional: custom result metadata (dict)"],"output_types":["persisted results (JSON/CSV files)","result metadata (structured data)","historical trend data (numeric)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Valid API credentials for target LLM provider (OpenAI key, Anthropic key, etc.)","Network access to LLM endpoints","Sufficient API quota/rate limits for test volume","Garak framework installed","Target LLM harness configured and authenticated","Optional: embedding model for semantic similarity detection","Bash shell (for CLI) or Python environment (for API)","YAML/JSON config file defining test suite","All target harnesses configured and authenticated"],"failure_modes":["Harness coverage limited to explicitly implemented providers — custom models require writing new harness code","Rate limiting and quota handling delegated to harness implementations — inconsistent behavior across providers","No built-in cost tracking — high-volume scanning against paid APIs can incur unexpected charges","Synchronous harness execution creates bottlenecks when scanning many models sequentially","Detection heuristics are often rule-based (keyword/regex matching) — brittle against paraphrased or obfuscated responses","Probe coverage is manually curated — emerging attack patterns require new probe implementations","No adaptive testing — probes don't learn from model responses to refine subsequent tests","Semantic detection relies on external models (e.g., embeddings) — adds latency and cost","CLI argument parsing is complex — steep learning curve for new users","Python API documentation is minimal — requires reading source code to understand usage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:25.872Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-garak","compare_url":"https://unfragile.ai/compare?artifact=pypi-garak"}},"signature":"l1AwBW+/lambXPj/EyNdTbNqOjPNOXuuQkS27DhGQSh1/uMfFeYJ1uUfKlvPmWVsSRZ2XJP128AAjrwWRSaoDg==","signedAt":"2026-06-21T15:45:03.247Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-garak","artifact":"https://unfragile.ai/pypi-garak","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-garak","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}