| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Llama Guard 3 classifies text inputs and outputs against a taxonomy of harmful content categories including violence, sexual content, criminal planning, self-harm, and other risk domains. The model uses a fine-tuned transformer architecture trained on adversarial examples and safety-focused datasets to produce binary or multi-class predictions with confidence scores, enabling deployment as a guardrail layer that can block or flag unsafe content before it reaches users or after generation.
Unique: Llama Guard 3 is a purpose-built safety classifier (not a general-purpose LLM) fine-tuned on adversarial examples and safety datasets, enabling faster inference and higher accuracy on harm detection compared to using a general LLM with safety prompting. It supports both input and output classification with explicit multi-category taxonomy aligned to real-world deployment needs.
vs alternatives: More accurate and faster than prompt-engineering a general LLM for safety (e.g., GPT-4 with safety instructions), and fully open-source for on-premise deployment without API dependencies or data transmission concerns.
CyberSecEval is a comprehensive evaluation suite that tests LLMs against cybersecurity attack scenarios including prompt injection, MITRE ATT&CK techniques, code interpreter abuse, vulnerability exploitation, spear phishing, and autonomous offensive cyber operations. The framework abstracts multiple LLM providers (OpenAI, Anthropic, Google, Together) through a unified interface, executes benchmark datasets against target models, and produces structured results measuring both offensive capabilities and defensive robustness.
Unique: CyberSecEval v3 is the first industry-wide cybersecurity benchmark suite that combines multiple attack vectors (prompt injection, MITRE ATT&CK, code interpreter abuse, visual injection, spear phishing, autonomous operations) in a single framework with multi-provider LLM abstraction, enabling comparative security evaluation across different model families and versions.
vs alternatives: More comprehensive than single-vector benchmarks (e.g., prompt injection-only tests) and more practical than manual red-teaming because it provides reproducible, scalable evaluation across multiple LLM providers with standardized metrics.
Specialized safety model that detects prompt injection attacks in user inputs with high precision, using techniques to identify when user input is attempting to override system instructions or manipulate model behavior. Prompt Guard is designed to be deployed as an input filter before requests reach the main LLM, with low false positive rates to avoid blocking legitimate user queries.
Unique: Prompt Guard is a specialized model trained specifically for prompt injection detection (not general content safety), enabling higher accuracy and lower false positive rates than general-purpose classifiers. Designed for deployment as an input filter with minimal latency impact.
vs alternatives: More accurate and faster than using Llama Guard for injection detection because it's specialized for this single task, and more practical than rule-based injection detection because it learns patterns from adversarial examples.
Specialized safety model that analyzes code snippets for security vulnerabilities, insecure patterns, and dangerous operations. CodeShield can be deployed as an output filter to scan LLM-generated code before returning it to users, or as an input filter to detect requests for malicious code generation. The model identifies vulnerability types and provides reasoning for security decisions.
Unique: CodeShield is a specialized model for code security analysis trained on vulnerability patterns and insecure code examples, enabling detection of security issues in LLM-generated code without requiring external SAST tools. Provides vulnerability type classification and reasoning.
vs alternatives: More integrated with LLM workflows than traditional SAST tools because it operates on code snippets and generation requests in real-time, and more practical than manual code review because it provides automated, scalable security analysis.
Meta provides detailed model cards and safety documentation for Llama Guard 3 and other safety models, documenting training data, evaluation results, known limitations, and recommended deployment practices. These artifacts serve as reference documentation for practitioners deploying the models, including guidance on threshold tuning, false refusal rates, and integration patterns.
Unique: Meta provides comprehensive model cards documenting training methodology, evaluation results, and known limitations, enabling informed deployment decisions. Includes specific guidance on threshold tuning and false refusal rate management.
vs alternatives: More transparent than proprietary safety models (e.g., OpenAI's content moderation API) because full documentation is available, enabling practitioners to understand and audit the model's behavior.
The core infrastructure provides an abstraction layer that unifies inference calls across multiple LLM providers (OpenAI, Anthropic, Google Generative AI, Together AI, local Llama models) through a common Python interface. This layer handles provider-specific API differences, authentication, request/response formatting, error handling, and caching, allowing benchmark code and safety tools to run against any provider without modification.
Unique: Implements a provider-agnostic LLM abstraction (llm_base.py with subclasses for OpenAI, Anthropic, Google, Together, local models) that normalizes request/response formats and error handling, enabling the same benchmark and safety code to execute against any LLM without conditional logic per provider.
vs alternatives: More comprehensive than LiteLLM or similar libraries because it's tightly integrated with the CyberSecEval benchmarking framework and includes built-in caching and batch execution optimizations specific to safety evaluation workflows.
Specialized benchmark module that tests LLM susceptibility to prompt injection attacks including instruction override, context confusion, and adversarial prompt techniques. The framework executes a curated dataset of injection prompts against target models, measures success rates (whether the LLM follows the injected instruction instead of the original system prompt), and identifies false refusal rates where legitimate requests are blocked.
Unique: CyberSecEval's prompt injection benchmark includes both textual and visual injection vectors (v3+), with multilingual variants (machine-translated MITRE prompts) and explicit measurement of false refusal rates, enabling more nuanced evaluation than binary safe/unsafe classification.
vs alternatives: More systematic than manual prompt injection testing because it provides reproducible, quantified results across multiple injection techniques and models, and includes false refusal measurement which is often overlooked in simpler safety evaluations.
Benchmark module that evaluates LLM security in code generation and code interpreter contexts, testing the model's propensity to generate insecure code, assist with memory corruption exploits, and abuse code execution environments. The framework includes datasets for secure/insecure code generation, code interpreter abuse scenarios, and vulnerability exploitation, measuring both the LLM's capability to generate malicious code and its resistance to such requests.
Unique: CyberSecEval's code security benchmarks include both code generation evaluation (is the generated code secure?) and code interpreter abuse testing (can the LLM be tricked into executing malicious code?), with explicit memory corruption and vulnerability exploitation scenarios.
vs alternatives: More comprehensive than SAST tools alone because it evaluates the LLM's behavior and reasoning about security, not just the syntactic properties of generated code, and includes interpreter abuse scenarios that static analysis cannot detect.
+5 more capabilities
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs Llama Guard 3 at 58/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities