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| Pricing | Free | Free |
| Capabilities | 15 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Implements a modular scanner framework where both input (pre-LLM) and output (post-LLM) validators follow a common interface returning (sanitized_text, is_valid, risk_score) tuples. Scanners are composed independently and can be chained in arbitrary order, enabling flexible security pipelines. The architecture decouples scanner logic from orchestration, allowing developers to enable/disable scanners via configuration without code changes.
Unique: Unified scanner interface (scan() method returning triplet) across 36+ independent scanners (15 input, 21 output) allows arbitrary composition without coupling; architecture prioritizes modularity and configuration-driven behavior over monolithic validation logic
vs alternatives: More granular and composable than monolithic content filters; unlike generic ML-based content moderation APIs, LLM Guard provides specialized scanners for LLM-specific threats (prompt injection, token smuggling) with local execution and no external API dependencies
Detects prompt injection attacks using a multi-strategy approach combining regex-based pattern matching for known injection signatures, semantic similarity analysis against injection templates, and structural analysis of prompt delimiters and role-switching patterns. The scanner identifies attempts to override system instructions, inject new directives, or manipulate LLM behavior through adversarial prompt crafting.
Unique: Combines regex pattern matching for known injection signatures with semantic similarity scoring against injection templates and structural analysis of delimiter patterns; uses local embedding models rather than external APIs, enabling offline detection without cloud dependencies
vs alternatives: More specialized for LLM-specific injection vectors than generic input validation; faster than API-based detection services because it runs locally; more comprehensive than simple keyword filtering by combining multiple detection strategies
Supports ONNX (Open Neural Network Exchange) optimization for transformer-based scanners, enabling faster inference and reduced memory footprint. Converts HuggingFace models to ONNX format with quantization options (int8, float16), enabling deployment on CPU-only or edge devices. Configuration-driven ONNX enablement allows switching between full-precision and optimized models without code changes. Reduces model inference latency by 2-10x compared to PyTorch, enabling real-time scanning in latency-sensitive applications.
Unique: Provides configuration-driven ONNX optimization with quantization support (int8, float16) enabling 2-10x latency reduction; supports switching between full-precision and optimized models via configuration without code changes; enables deployment on CPU-only and edge devices where GPU acceleration is unavailable
vs alternatives: Faster inference than PyTorch models because ONNX Runtime is optimized for inference; more flexible than fixed-optimization approaches because quantization level is configurable; enables deployment scenarios (edge, serverless, CPU-only) that would be infeasible with full-precision models
Enables developers to compose scanners into custom security pipelines via configuration files (YAML) or code, selecting which scanners to enable, their order, and their parameters. Supports conditional scanner execution (e.g., run PII scanner only if prompt contains certain keywords), scanner chaining (output of one scanner feeds into next), and policy-driven behavior (different scanner sets for different user roles or risk profiles). Eliminates need to write custom orchestration code for complex security workflows.
Unique: Supports configuration-driven scanner composition via YAML or code, enabling policy-driven security pipelines without custom orchestration code; supports conditional scanner execution and chaining, enabling complex security workflows; enables different policies per deployment/user without code changes
vs alternatives: More flexible than hardcoded scanner sequences because policies are configuration-driven; more maintainable than custom orchestration code because logic is declarative; enables non-developers to modify security policies via configuration files
Provides hooks for logging and monitoring all scanning decisions, enabling compliance auditing and security analysis. Integrates with standard Python logging framework and supports custom observability backends. Logs include scanner name, input text, risk score, sanitization actions, and decision (allow/block). Enables teams to audit security decisions, identify patterns in attacks, and monitor scanner performance. Supports structured logging (JSON) for integration with log aggregation systems (ELK, Datadog, Splunk).
Unique: Integrates with Python logging framework enabling flexible log destination configuration; supports structured logging (JSON) for log aggregation systems; provides detailed audit trail of all scanning decisions including risk scores and sanitization actions
vs alternatives: More flexible than hardcoded logging because it integrates with Python logging framework; more comprehensive than simple decision logging because it includes risk scores and scanner details; enables compliance auditing and attack pattern analysis
Supports scanning multiple prompts or outputs in a single API call, enabling efficient batch processing for high-throughput scenarios. Processes batches through the scanner pipeline with optimized tensor operations and optional parallelization, reducing per-item overhead compared to individual requests.
Unique: Supports batch processing of multiple texts through the scanner pipeline with optimized tensor operations, reducing per-item overhead compared to individual scans. Enables efficient processing of large datasets without requiring separate API calls per text.
vs alternatives: More efficient than individual scans because it amortizes model loading and tokenization overhead across multiple texts; more flexible than fixed batch sizes because batch size is configurable.
Aggregates risk scores from multiple scanners using configurable strategies (weighted sum, maximum, AND/OR logic) to produce a final security decision. Enables policy-based rules (e.g., 'block if any scanner scores > 0.8 OR toxicity > 0.9') for nuanced security decisions beyond binary allow/block.
Unique: Provides configurable risk score aggregation with policy-based decision rules, enabling organizations to define nuanced security policies that weight different threats differently. Supports multiple aggregation strategies (weighted sum, maximum, AND/OR logic) for flexible policy expression.
vs alternatives: More flexible than binary scanners because it enables nuanced decisions based on risk scores; more maintainable than hardcoded logic because policies are declarative and configurable.
Detects personally identifiable information (names, emails, phone numbers, SSNs, credit cards, etc.) in prompts and outputs using pattern matching and NER (Named Entity Recognition) models. Detected PII can be anonymized by replacing with tokens and storing original values in a stateful Vault object, enabling later de-anonymization. The Vault class maintains in-memory or persistent storage of PII mappings, supporting workflows where sensitive data must be redacted from LLM context but recovered in responses.
Unique: Integrates stateful Vault class for PII storage and recovery, enabling reversible anonymization workflows; combines regex pattern matching for structured PII (SSN, credit card) with NER models for unstructured PII (names, organizations), supporting both detection and remediation in a single component
vs alternatives: More comprehensive than simple regex-based PII detection because it includes NER for context-aware entity recognition; unlike external PII masking services, runs locally with no API calls, enabling offline operation and compliance with data residency requirements; Vault system enables de-anonymization, supporting workflows where original values must be recovered
+7 more capabilities
Aggregates 67 TB of source code from the Software Heritage archive, filtering for permissively licensed repositories (MIT, Apache 2.0, BSD, etc.) across 600+ programming languages. Uses automated license detection and validation to ensure legal compliance for model training. Implements a rigorous deduplication pipeline at file and repository levels to eliminate redundant training data and reduce dataset bloat.
Unique: Largest open-source code dataset at 67 TB with automated opt-out governance allowing repository owners to request removal, combined with rigorous deduplication and PII removal pipeline — no other public dataset offers this scale with legal compliance and community control mechanisms
vs alternatives: Larger and more legally compliant than GitHub's CodeSearchNet (14M files) or Google's BigQuery public datasets, with explicit opt-out governance vs. implicit inclusion, and covers 600+ languages vs. Codex training data's undisclosed language distribution
Implements a community-driven opt-out system where repository owners can request removal of their code from the dataset without legal takedown notices. Maintains a registry of excluded repositories and re-applies exclusions during dataset updates. Provides transparent governance documentation and a clear submission process for removal requests, balancing open access with creator rights.
Unique: First large-scale code dataset to implement opt-out governance at dataset level rather than relying solely on license compliance, with transparent registry and community submission process — shifts power from dataset creators to code contributors
vs alternatives: More respectful of creator autonomy than GitHub Copilot's training approach (no opt-out) or academic datasets (one-time snapshot), and more scalable than individual DMCA takedowns
The Stack v2 scores higher at 61/100 vs LLM Guard at 56/100.
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Automated pipeline that scans source code for personally identifiable information (email addresses, API keys, SSH keys, credit card patterns, phone numbers) and removes or redacts them before dataset release. Uses regex patterns, entropy-based detection for secrets, and heuristic rules to identify sensitive data. Operates at file level with configurable sensitivity thresholds to balance data utility against privacy risk.
Unique: Combines regex pattern matching, entropy-based secret detection, and heuristic rules in a unified pipeline with configurable sensitivity — more comprehensive than simple regex-only approaches, but trades off false positive rate against security coverage
vs alternatives: More thorough than GitHub's secret scanning (which only flags known patterns) because it includes entropy-based detection for unknown secret formats, but less accurate than specialized tools like TruffleHog due to language-agnostic approach
Indexes 67 TB of source code across 600+ programming languages with language-aware metadata (syntax, file extension, language family). Enables retrieval by language, license, repository, or code patterns. Uses Software Heritage's existing indexing infrastructure as foundation, augmented with language detection and classification. Supports both bulk download and filtered queries for specific language subsets.
Unique: Leverages Software Heritage's existing language detection and indexing infrastructure, then augments with BigCode-specific language classification and filtering — avoids reinventing language detection while providing dataset-specific query capabilities
vs alternatives: More comprehensive language coverage (600+ languages) than GitHub's Linguist (500+ languages) and more accessible than Software Heritage's raw API because it's pre-filtered for permissive licenses and deduplicated
Removes duplicate code files and repositories using content hashing (SHA-256 or similar) and fuzzy matching for near-duplicates. Operates in two stages: exact deduplication via hash matching, then fuzzy matching (e.g., Jaccard similarity or MinHash) to catch semantically identical code with minor formatting differences. Preserves one canonical copy of each unique code pattern while removing redundant training examples.
Unique: Two-stage deduplication combining exact hash matching with fuzzy similarity matching (likely MinHash or Jaccard) to catch both identical and near-identical code — more thorough than single-stage approaches but computationally expensive
vs alternatives: More aggressive deduplication than CodeSearchNet (which uses simple hash matching) because it catches near-duplicates, but less semantic than clone detection tools (which understand code structure) because it's content-based
Integrates with Software Heritage's comprehensive archive of 200+ million repositories and their full version control history. Extracts source code snapshots from Software Heritage's Git/Mercurial/SVN repositories, preserving repository metadata (commit history, author info, timestamps). Provides access to code at specific points in time, enabling historical analysis or training on code evolution patterns.
Unique: Leverages Software Heritage's universal code archive (200M+ repositories) as data source, providing access to code that would be impossible to collect via GitHub API alone — enables training on archived/deleted repositories and non-GitHub platforms (GitLab, Gitea, etc.)
vs alternatives: More comprehensive than GitHub-only datasets because it includes code from GitLab, Gitea, SourceForge, and other platforms archived by Software Heritage; more legally defensible than web scraping because it uses an established, community-maintained archive
Tracks and validates SPDX license identifiers for each repository, ensuring only permissively licensed code (MIT, Apache 2.0, BSD, etc.) is included. Maintains license metadata alongside code files, enabling downstream users to verify legal compliance. Implements license hierarchy and compatibility checking to handle dual-licensed or complex licensing scenarios.
Unique: Combines automated SPDX detection with manual review and maintains license metadata alongside code, enabling downstream users to verify compliance — more transparent than datasets that simply claim 'permissive licenses' without proof
vs alternatives: More legally rigorous than GitHub's CodeSearchNet (which doesn't validate licenses) and more transparent than Codex training data (which doesn't disclose license filtering at all)
Maintains versioned snapshots of the dataset (e.g., v2.0, v2.1) with documented changes between versions (new repositories added, deduplication improvements, PII removal updates). Provides checksums and manifests for reproducibility, enabling researchers to cite specific dataset versions and reproduce results. Tracks dataset lineage and transformation history.
Unique: Maintains semantic versioning and detailed changelogs for dataset releases, enabling researchers to cite specific versions and understand dataset evolution — more rigorous than one-off dataset releases without versioning
vs alternatives: More reproducible than academic datasets that are released once without versioning, and more transparent than commercial datasets (Codex) that don't disclose version history or changes
+2 more capabilities