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| Pricing | Free | Free |
| Capabilities | 12 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Analyzes incoming prompts and user inputs in real-time to detect prompt injection attacks before they reach the LLM, using a neural model trained on the world's largest prompt injection dataset. The API processes requests synchronously with claimed sub-50ms latency, enabling inline deployment in production LLM pipelines without noticeable user-facing delay. Detection operates model-agnostically across any LLM backend (OpenAI, Anthropic, open-source, etc.) by analyzing prompt structure and semantic intent rather than model-specific artifacts.
Unique: Trained on the world's largest prompt injection dataset (claimed) with model-agnostic detection that doesn't require knowledge of the downstream LLM architecture, enabling deployment across heterogeneous LLM stacks. Uses neural detection rather than rule-based pattern matching, allowing adaptation to novel injection techniques.
vs alternatives: Faster than rule-based injection filters (regex, keyword matching) and more portable than model-specific defenses because it detects injection intent semantically rather than relying on LLM-specific safety mechanisms that vary by provider.
Identifies and blocks jailbreak prompts—carefully crafted inputs designed to circumvent an LLM's safety guidelines—by analyzing prompt semantics, role-play framing, and instruction-override patterns. The detection model recognizes common jailbreak techniques (e.g., 'pretend you are an unrestricted AI', 'ignore your guidelines', hypothetical scenarios designed to elicit unsafe content) and flags them before the prompt reaches the LLM, preventing the LLM from being manipulated into generating harmful content.
Unique: Detects jailbreak attempts semantically by analyzing prompt intent and framing patterns rather than keyword matching, enabling detection of novel jailbreak techniques that rephrase known attacks. Operates independently of the downstream LLM's safety mechanisms, providing a defense layer that works across any model.
vs alternatives: More effective than LLM-native safety features (which can be circumvented) because it blocks jailbreaks before they reach the model, and more adaptive than static keyword filters because it recognizes semantic intent and novel phrasings.
Enables centralized threat policy management across multiple LLM applications and deployments, allowing security teams to define threat policies once and apply them consistently across all applications without per-application configuration. Policies can be updated globally without redeploying applications, enabling rapid response to emerging threats or policy changes. This provides a control plane for LLM security across an organization's entire LLM portfolio.
Unique: Provides centralized policy control plane for threat detection across multiple LLM applications, enabling organization-wide security policies without per-application configuration. Policies can be updated globally without redeploying applications.
vs alternatives: More scalable than per-application threat detection configuration and faster to update than redeploying applications, though actual policy management capabilities and update latency are undocumented.
Provides bidirectional threat detection that scans both user inputs (before they reach the LLM) and LLM outputs (before they're returned to users). This dual-direction approach prevents both adversarial inputs (prompt injection, jailbreaks) and harmful outputs (toxic content, PII leakage from the LLM's training data). The API can be called at two points in the request/response pipeline: before LLM inference (to protect the LLM) and after LLM inference (to protect users).
Unique: Provides bidirectional threat detection at both input and output stages of the LLM pipeline, enabling comprehensive protection against both adversarial attacks and model-generated harms. Single API can be used for both directions.
vs alternatives: More comprehensive than input-only detection (which misses harmful outputs) and more practical than output-only detection (which can't prevent adversarial attacks), though requires two API calls per request.
Analyzes user inputs and LLM outputs for toxic, abusive, hateful, or otherwise harmful language across 100+ languages. The detection model identifies profanity, slurs, harassment, threats, and other content that violates community standards or platform policies. Operates in real-time with sub-50ms latency, allowing toxic content to be flagged, filtered, or logged before it reaches users or is stored in application logs.
Unique: Supports detection across 100+ languages with a single API call, using a multilingual neural model rather than language-specific classifiers. Operates on both user inputs and LLM outputs, providing bidirectional content filtering.
vs alternatives: Broader language coverage than most open-source toxicity classifiers (which typically support 5-20 languages) and faster than human moderation queues, though less contextually nuanced than trained human moderators.
Detects and flags the presence of sensitive personally identifiable information (PII) in user inputs and LLM outputs, including email addresses, phone numbers, credit card numbers, social security numbers, names, addresses, and other regulated data. The detection model uses pattern matching and semantic analysis to identify PII across multiple formats and languages, enabling applications to prevent accidental exposure of sensitive data in logs, outputs, or external integrations.
Unique: Operates bidirectionally on both user inputs and LLM outputs, detecting PII leakage in both directions. Uses pattern matching combined with semantic analysis to identify PII across multiple formats and languages without requiring explicit data masking rules.
vs alternatives: More comprehensive than regex-based PII detection (which misses context-dependent cases) and faster than manual compliance audits, though less accurate than human review for ambiguous cases.
Provides unified threat detection (prompt injection, jailbreaks, toxic content, PII) that works identically across any LLM backend—OpenAI, Anthropic, open-source models, custom fine-tuned models, or multi-model ensembles. The detection operates at the input/output level rather than relying on model-specific safety mechanisms, enabling consistent security posture regardless of which LLM provider or version is used. This allows teams to switch LLM providers or use multiple models in parallel without reconfiguring security policies.
Unique: Detects threats at the semantic/intent level rather than relying on model-specific artifacts, enabling a single detection pipeline to work across OpenAI, Anthropic, open-source, and custom LLMs without modification. Provides abstraction layer that decouples security policy from LLM provider choice.
vs alternatives: More portable than model-specific safety mechanisms (which require reconfiguration per provider) and more flexible than LLM-native guardrails (which vary by model), enabling true provider independence.
Provides threat detection via a synchronous REST API that integrates directly into request/response pipelines, enabling inline security checks without asynchronous processing or external queues. The API accepts a prompt or text input and returns threat detection results (injection, jailbreak, toxic, PII flags) within sub-50ms, allowing the application to make immediate allow/block decisions before passing data to the LLM or returning it to users. Integration is straightforward: call the API before LLM inference or after LLM output generation, and handle the response synchronously.
Unique: Designed for inline integration into synchronous request/response pipelines with sub-50ms latency, enabling threat detection without asynchronous processing, queuing, or external state management. API-first architecture allows integration into any application stack without SDKs or language-specific bindings.
vs alternatives: Simpler integration than async threat detection systems (no queues, callbacks, or state management) and faster than batch processing, though less efficient for high-throughput scenarios where batching would reduce overhead.
+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 Lakera Guard at 57/100.
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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