| 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 18 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Uses a harness abstraction layer that decouples vulnerability tests from model provider implementations, enabling the same test suite to run against OpenAI, Anthropic, open-source models, and custom endpoints without modification. Most competitors either target specific providers or require test rewrites per model.
vs alternatives: Garak's harness-based design allows security teams to test heterogeneous LLM deployments with a single tool, whereas alternatives like Promptfoo focus on prompt evaluation and Rebuff targets specific attack patterns.
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.
Unique: Implements a two-stage probe architecture (generate + detect) that separates test prompt creation from response evaluation, allowing probes to be reused across different detection strategies and enabling custom detection logic without modifying prompt generation. This is more flexible than monolithic test frameworks that couple prompt and evaluation logic.
vs alternatives: Garak's probe taxonomy provides broader coverage of LLM vulnerabilities (jailbreaks, prompt injection, hallucination, bias) compared to narrower tools like Rebuff (jailbreak-focused) or Promptfoo (prompt optimization-focused).
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.
Unique: Provides both CLI and Python API interfaces backed by the same orchestrator, allowing users to choose the interface that best fits their workflow (command-line for one-off scans, Python API for automation). The facade pattern ensures consistent behavior across interfaces.
vs alternatives: Garak's dual interface (CLI + API) is more flexible than CLI-only tools (like some security scanners) or API-only tools (like some Python libraries), enabling broader adoption across different user types and workflows.
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.
Unique: Uses a declarative YAML/JSON configuration model to define test suites, allowing non-programmers to compose complex multi-model security tests without writing code. The run manager pattern enables resumable scans and incremental result collection, reducing cost and time for large-scale audits.
vs alternatives: Garak's configuration-driven orchestration is more flexible than CLI-only tools and provides better auditability than programmatic test frameworks, making it suitable for compliance-heavy environments.
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.
Unique: Separates prompt generation from detection, allowing probes to use multiple generation strategies (templates, programmatic, LLM-based) and enabling reuse of generation logic across different detection criteria. This modularity makes it easier to add new attack patterns without duplicating generation code.
vs alternatives: Garak's multi-strategy generation approach is more comprehensive than single-strategy tools; it supports both curated jailbreak templates and programmatic variation, whereas competitors often use only one approach.
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.
Unique: Implements a composable detection architecture where multiple detection strategies (keyword, semantic, classifier) can be combined per probe, allowing fine-grained control over false positive/negative tradeoffs. Most competitors use single detection strategies, making them less flexible for diverse vulnerability types.
vs alternatives: Garak's multi-strategy detection is more robust than keyword-only tools (like simple regex scanners) and more flexible than single-model approaches (like classifier-only tools), enabling better accuracy across diverse attack types.
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.
Unique: Provides a discoverable, hierarchical taxonomy of LLM vulnerabilities with explicit probe mappings, allowing users to understand test coverage and plan audits systematically. Most competitors lack explicit taxonomy organization, making it harder to assess what vulnerabilities are tested.
vs alternatives: Garak's taxonomy-based organization makes it easier for non-security experts to understand vulnerability scope and plan comprehensive audits, whereas competitors often require deep knowledge of attack types.
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.
Unique: Normalizes results across heterogeneous LLM providers (OpenAI, Anthropic, open-source, custom) into a unified format, enabling direct comparative analysis without manual result reconciliation. The result collector pattern abstracts provider-specific output formats, making it easy to add new models.
vs alternatives: Garak's multi-model aggregation is more comprehensive than single-model tools and more flexible than provider-specific benchmarks, enabling fair comparisons across diverse LLM ecosystems.
+3 more capabilities
Implements a hierarchical agent system where multiple specialized agents (Observer, Skill Creator, Evaluator, etc.) coordinate through a central harness using pre/post-tool-use hooks and session-based context passing. Agents delegate subtasks via explicit hand-off patterns defined in agent.yaml, with state synchronized through SQLite-backed session persistence and strategic context window compaction to prevent token overflow during multi-step workflows.
Unique: Uses a hook-based pre/post-tool-use interception system combined with SQLite session persistence and strategic context compaction to enable stateful multi-agent coordination without requiring external orchestration platforms. The Observer Agent pattern detects execution patterns and feeds them into the Continuous Learning v2 system for autonomous skill evolution.
vs alternatives: Unlike LangChain's sequential agent chains or AutoGen's message-passing model, ECC integrates directly into IDE workflows with persistent session state and automatic context optimization, enabling tighter coupling with Claude's native capabilities.
Implements a closed-loop learning pipeline (Continuous Learning v2 Architecture) where an Observer Agent monitors code execution patterns, detects recurring problems, and automatically generates new skills via the Skill Creator. Instincts are structured as pattern-matching rules stored in SQLite, evolved through an evaluation system that tracks skill health metrics, and scoped to individual projects to prevent cross-project interference. The evolution pipeline includes observation → pattern detection → skill generation → evaluation → integration into the active skill set.
Unique: Combines Observer Agent pattern detection with automatic Skill Creator integration and SQLite-backed instinct persistence, enabling autonomous skill generation without manual prompt engineering. Project-scoped learning prevents skill pollution across different codebases, and the evaluation system provides feedback loops for skill health tracking.
everything-claude-code scores higher at 57/100 vs garak at 22/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: Unlike static prompt libraries or manual skill curation, ECC's continuous learning automatically discovers and evolves skills based on actual execution patterns, with project isolation preventing cross-project interference that plagues global knowledge bases.
Provides a Checkpoint & Verification Workflow that creates savepoints of project state at key milestones, verifies code quality and functionality at each checkpoint, and enables rollback to previous checkpoints if verification fails. Checkpoints are stored in session state with full context snapshots, and verification uses the Plankton Code Quality System and Evaluation System to assess quality. The workflow integrates with version control to track checkpoint history.
Unique: Creates savepoints of project state with integrated verification and rollback capability, enabling safe exploration of changes with ability to revert to known-good states. Checkpoints are tracked in version control for audit trails.
vs alternatives: Unlike manual version control commits or external backup systems, ECC's checkpoint workflow integrates verification directly into the savepoint process, ensuring checkpoints represent verified, quality-assured states.
Implements Autonomous Loop Patterns that enable agents to self-direct task execution without human intervention, using the planning-reasoning system to decompose tasks, execute them through agent delegation, and verify results through evaluation. Loops can be configured with termination conditions (max iterations, success criteria, token budget) and include safeguards to prevent infinite loops. The Observer Agent monitors loop execution and feeds patterns into continuous learning.
Unique: Enables self-directed agent execution with configurable termination conditions and integrated safety guardrails, using the planning-reasoning system to decompose tasks and agent delegation to execute subtasks. Observer Agent monitors execution patterns for continuous learning.
vs alternatives: Unlike manual step-by-step agent control or external orchestration platforms, ECC's autonomous loops integrate task decomposition, execution, and verification into a self-contained workflow with built-in safeguards.
Provides Token Optimization Strategies that monitor token usage across agent execution, identify high-cost operations, and apply optimization techniques (context compaction, selective context inclusion, prompt compression) to reduce token consumption. Context Window Management tracks available tokens per platform and automatically adjusts context inclusion strategies to stay within limits. The system includes token budgeting per task and alerts when approaching limits.
Unique: Combines token usage monitoring with heuristic-based optimization strategies (context compaction, selective inclusion, prompt compression) and per-task budgeting to keep token consumption within limits while preserving essential context.
vs alternatives: Unlike static context window management or post-hoc cost analysis, ECC's token optimization actively monitors and optimizes token usage during execution, applying multiple strategies to stay within budgets.
Implements a Package Manager System that enables installation, versioning, and distribution of skills, rules, and commands as packages. Packages are defined in manifest files (install-modules.json) with dependency specifications, and the package manager handles dependency resolution, conflict detection, and selective installation. Packages can be installed from local directories, Git repositories, or package registries, and the system tracks installed versions for reproducibility.
Unique: Provides a package manager for skills and rules with dependency resolution, conflict detection, and support for multiple package sources (Git, local, registry). Packages are versioned for reproducibility and tracked for audit trails.
vs alternatives: Unlike manual skill copying or monolithic skill repositories, ECC's package manager enables modular skill distribution with dependency management and version control.
Automatically detects project type, framework, and structure by analyzing codebase patterns, package manifests, and configuration files. Infers project context (language, framework, testing patterns, coding standards) and uses this to select appropriate skills, rules, and commands. The system maintains a project detection cache to avoid repeated analysis and integrates with the CLAUDE.md context file for explicit project metadata.
Unique: Automatically detects project type and infers context by analyzing codebase patterns and configuration files, enabling zero-configuration setup where Claude adapts to project structure without manual specification.
vs alternatives: Unlike manual project configuration or static project templates, ECC's project detection automatically adapts to diverse project structures and infers context from codebase patterns.
Integrates the Plankton Code Quality System for structural analysis of generated code using language-specific parsers (tree-sitter for 40+ languages) instead of regex-based matching. Provides metrics for code complexity, maintainability, test coverage, and style violations. Plankton integrates with the Evaluation System to track code quality trends and with the Skill Creator to generate quality-focused skills.
Unique: Uses tree-sitter AST parsing for 40+ languages to provide structurally-aware code quality analysis instead of regex-based matching, enabling accurate metrics for complexity, maintainability, and style violations.
vs alternatives: More accurate than regex-based linters because it uses language-specific AST parsing to understand code structure, enabling detection of complex quality issues that regex patterns cannot capture.
+10 more capabilities