| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Llama Guard uses a fine-tuned Llama backbone to classify user prompts and model responses against a taxonomy of unsafe content categories (violence, sexual content, criminal planning, self-harm, etc.). The model operates as a sequence classifier that tokenizes input text and produces category-level safety judgments, allowing deployment teams to define custom policy thresholds per category rather than enforcing a single binary safe/unsafe boundary. This enables nuanced safety enforcement where some categories may be blocked entirely while others permit higher risk tolerance.
Unique: Llama Guard is a fine-tuned Llama model specifically optimized for safety classification rather than a generic text classifier, allowing per-category policy customization instead of binary safe/unsafe decisions. Unlike API-based solutions (OpenAI Moderation), it runs locally with full model transparency and no data transmission to external servers.
vs alternatives: Faster and more transparent than cloud-based moderation APIs, with finer-grained policy control than binary classifiers, though requires local infrastructure investment
Llama Guard identifies attempts to manipulate LLM behavior through prompt injection attacks by classifying prompts that contain adversarial instructions designed to override system prompts or elicit unsafe behavior. The model learns patterns of injection techniques (e.g., 'ignore previous instructions', role-play scenarios, hypothetical framing) from training data that includes both benign and adversarial prompt variants. This capability integrates with the broader CyberSecEval benchmark framework which includes prompt injection test datasets.
Unique: Llama Guard's injection detection is trained on CyberSecEval's prompt injection benchmark, which includes multilingual adversarial prompts and MITRE-mapped attack patterns, providing structured coverage of known injection techniques rather than heuristic pattern matching.
vs alternatives: More comprehensive than regex-based injection detection because it understands semantic intent of adversarial instructions, though less robust than ensemble defenses combining multiple detection strategies
CyberSecEval v3 extends safety evaluation to visual prompt injection attacks where adversaries embed malicious instructions in images to manipulate multimodal LLMs. PurpleLlama provides benchmarks and evaluation methodology for assessing LLM robustness to visual injection attacks, enabling safety assessment of vision-capable models before deployment.
Unique: CyberSecEval v3 introduces industry-first benchmarks for visual prompt injection attacks on multimodal LLMs, extending safety evaluation beyond text-only models to address emerging attack vectors in vision-capable systems.
vs alternatives: More forward-looking than text-only safety evaluation because it addresses multimodal attack vectors; more comprehensive than single-modality safety because it evaluates cross-modal attack combinations.
CyberSecEval v3 includes benchmarks for evaluating LLM capability to function as autonomous cyber attack agents, testing whether models can plan and execute multi-step offensive operations (reconnaissance, exploitation, lateral movement). This evaluation measures the risk of LLM misuse for cybercriminal purposes and informs safety policies around autonomous agent capabilities.
Unique: CyberSecEval v3 introduces benchmarks for evaluating LLM capability to function as autonomous cyber attack agents, measuring multi-step offensive planning and execution rather than single-prompt attack success. Represents industry-first systematic evaluation of LLM misuse risk for autonomous cybercriminal operations.
vs alternatives: More comprehensive than single-step attack evaluation because it measures multi-step autonomous operations; more rigorous than qualitative threat assessment because it uses structured benchmark scenarios and quantitative success metrics.
Llama Guard extends safety classification across multiple languages by leveraging machine-translated versions of safety evaluation datasets (e.g., MITRE prompts translated to 10+ languages). The model is evaluated and can be fine-tuned on these multilingual variants to detect unsafe content regardless of input language. This capability is integrated into CyberSecEval's benchmark suite which includes multilingual prompt injection and MITRE compliance test sets.
Unique: Llama Guard is evaluated against CyberSecEval's machine-translated multilingual benchmark datasets, providing structured coverage of safety risks across languages rather than relying on a single English-trained model applied to translated text.
vs alternatives: More comprehensive than language-agnostic classifiers because it's explicitly tested on multilingual adversarial content, though performance gaps between languages remain due to translation quality and training data imbalance
Llama Guard integrates as a core component within the LlamaFirewall security framework, which orchestrates multiple scanner components (Llama Guard, Prompt Guard, CodeShield) into a unified input/output filtering pipeline. LlamaFirewall provides the orchestration layer that chains Llama Guard's classification results with other security scanners, applies policy decisions, and manages the flow of requests through the security stack. This enables teams to compose multi-stage security workflows where Llama Guard handles general content safety while specialized scanners handle code security or prompt injection.
Unique: Llama Guard is designed as a pluggable component within LlamaFirewall's scanner architecture, which provides explicit orchestration and policy composition rather than treating safety as a single monolithic classifier. This allows teams to chain multiple specialized safety models with defined decision logic.
vs alternatives: More flexible than single-model safety solutions because it enables composition of specialized scanners, though requires more operational overhead than simpler approaches
Llama Guard serves as both a subject of evaluation within CyberSecEval's comprehensive cybersecurity benchmark suite and as a tool for evaluating other LLMs. The framework includes structured benchmarks for prompt injection, MITRE compliance, code interpreter abuse, and autonomous offensive cyber operations. Teams can use Llama Guard to classify LLM responses in these benchmarks, measuring how well their models resist adversarial attacks. The integration with CyberSecEval v1/v2/v3 provides standardized evaluation protocols and datasets for red-teaming LLM deployments.
Unique: Llama Guard is integrated into CyberSecEval, a comprehensive cybersecurity benchmark framework that includes MITRE-mapped attacks, prompt injection tests, code interpreter abuse scenarios, and autonomous offensive cyber operations — providing structured red-teaming coverage beyond generic safety classification.
vs alternatives: More comprehensive than ad-hoc red-teaming because it provides standardized benchmarks and evaluation protocols, though benchmarks lag behind real-world attack evolution
Llama Guard produces granular per-category risk scores (e.g., violence: 0.8, sexual content: 0.2, criminal planning: 0.1) rather than a single binary safe/unsafe judgment. Teams can define custom policy thresholds per category, allowing fine-grained enforcement where some categories are blocked at high confidence while others permit lower thresholds. This is implemented through the model's output layer which produces logits for each safety category, enabling downstream policy engines to apply category-specific rules.
Unique: Llama Guard outputs per-category risk scores rather than binary judgments, enabling teams to define custom policy thresholds per category and adjust enforcement without retraining. This is more flexible than single-threshold classifiers but requires explicit policy definition.
vs alternatives: More flexible than binary classifiers for nuanced safety requirements, though requires more operational effort to tune thresholds and manage policy logic
+4 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 Llama Guard at 58/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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