LLM Guard vs endee
Side-by-side comparison to help you choose.
| Feature | LLM Guard | endee |
|---|---|---|
| Type | Framework | Repository |
| UnfragileRank | 43/100 | 30/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Implements a modular scanner framework where input scanners validate user prompts before LLM processing and output scanners validate LLM responses before user delivery. Each scanner follows a common interface returning (sanitized_text, is_valid, risk_score), enabling independent composition and chaining of 36+ security checks across both gates without tight coupling.
Unique: Implements a standardized scanner interface (scan() method returning triplet: sanitized_text, is_valid, risk_score) that decouples security logic from orchestration, enabling independent scanner development and composition without framework changes. This contrasts with monolithic validation approaches that embed multiple checks in a single function.
vs alternatives: More flexible than single-purpose filters because scanners are independently composable and returnable risk scores enable downstream decision-making; more modular than custom middleware because the common interface eliminates integration boilerplate.
Detects prompt injection attacks using multiple techniques including transformer-based semantic similarity matching, token-level pattern detection, and instruction-following analysis. Scanners analyze prompt structure to identify attempts to override system instructions or inject hidden commands through various encoding schemes and linguistic tricks.
Unique: Combines transformer-based semantic similarity scoring with token-level pattern matching to detect both obvious and obfuscated injection attempts. Uses HuggingFace model infrastructure with optional ONNX quantization for production inference speed, rather than relying solely on regex or keyword matching.
vs alternatives: More comprehensive than regex-based injection detection because it understands semantic intent; faster than full LLM-based detection because it uses lightweight transformer models optimized for classification rather than generation.
Allows teams to define custom scanner pipelines by composing multiple scanners with configurable execution order, conditional logic, and aggregation strategies. Supports YAML-based configuration for declaring which scanners to run, their parameters, and how to combine results (e.g., fail-fast on first violation, aggregate all risk scores).
Unique: Provides YAML-based configuration for declaring scanner pipelines, enabling non-developers to compose security policies without writing code. Supports configurable aggregation strategies for combining results from multiple scanners.
vs alternatives: More flexible than hardcoded scanner chains because configuration can be changed without redeployment; more accessible than programmatic composition because YAML is easier for non-technical users to understand.
Provides built-in observability hooks for tracking scanner execution, latency, and results. Exports structured metrics (execution time, risk scores, detection rates) for monitoring and alerting. Supports integration with observability platforms for tracking security events and identifying attack patterns.
Unique: Provides structured logging and metrics export hooks throughout the scanner framework, enabling integration with external observability platforms without custom instrumentation. Tracks both performance metrics (latency) and security metrics (detection rates).
vs alternatives: More comprehensive than basic logging because it exports structured metrics suitable for monitoring dashboards; more flexible than hardcoded metrics because hooks allow custom metric collection.
Abstracts transformer model loading through a unified interface (transformers_helpers module) that handles HuggingFace model downloads, caching, tokenization, and device placement (CPU/GPU). Automatically manages model lifecycle including lazy loading, memory management, and version pinning to ensure reproducible security scanning.
Unique: Provides a unified model loading interface (transformers_helpers) that abstracts HuggingFace model management, including caching, device placement, and tokenization. Enables lazy loading and model sharing across multiple scanners to optimize memory usage.
vs alternatives: More convenient than direct HuggingFace API usage because it handles caching and device placement automatically; more efficient than loading models per-scanner because it enables model sharing across multiple scanners.
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.
Identifies personally identifiable information (names, emails, phone numbers, SSNs, credit cards, etc.) in both prompts and outputs using pattern matching and NER models, then stores detected PII in a stateful Vault object for later retrieval or replacement. Enables reversible anonymization workflows where sensitive data is replaced with tokens and can be restored post-processing.
Unique: Implements a stateful Vault class that stores detected PII for reversible anonymization, enabling workflows where sensitive data is replaced with tokens and later restored. This contrasts with stateless PII removal that permanently deletes sensitive information without recovery capability.
vs alternatives: More flexible than simple redaction because Vault enables reversible anonymization for multi-turn conversations; more accurate than regex-only detection because it optionally uses NER models for context-aware entity recognition.
+7 more capabilities
Implements client-side encryption for vector embeddings before transmission to a remote database, using symmetric encryption (likely AES-256-GCM or similar) with key management handled entirely on the client. Vectors are encrypted at rest and in transit, with decryption occurring only after retrieval on the client side. This architecture ensures the database server never has access to plaintext vectors or their semantic content, enabling privacy-preserving similarity search without trusting the backend infrastructure.
Unique: Implements client-side encryption for vector embeddings with transparent key management in TypeScript, enabling encrypted similarity search without exposing vector semantics to the database server — a rare architectural pattern in vector database clients that typically assume trusted infrastructure
vs alternatives: Provides stronger privacy guarantees than Pinecone or Weaviate's native encryption (which encrypt at rest but expose vectors to the server during queries) by ensuring the server never handles plaintext vectors, though at the cost of client-side computational overhead
Executes similarity search queries against encrypted vector embeddings using approximate nearest neighbor (ANN) algorithms, likely implementing locality-sensitive hashing (LSH), product quantization, or HNSW-compatible approaches adapted for encrypted data. The client constructs encrypted query vectors and retrieves candidate results from the backend, then decrypts and re-ranks results locally to ensure accuracy despite the encryption layer. This enables semantic search without the server inferring query intent.
Unique: Adapts approximate nearest neighbor search algorithms to work with encrypted vectors by performing server-side ANN on ciphertext and client-side re-ranking on decrypted results, maintaining privacy while leveraging ANN efficiency — most vector databases either skip ANN for encrypted data or don't support encryption at all
vs alternatives: Enables semantic search with stronger privacy than Weaviate's encrypted search (which still exposes vectors during query processing) while maintaining better performance than fully homomorphic encryption approaches that are computationally prohibitive
LLM Guard scores higher at 43/100 vs endee at 30/100. LLM Guard leads on adoption, while endee is stronger on ecosystem.
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Validates vector dimensions against expected embedding model output sizes and checks compatibility between query vectors and stored vectors before operations, preventing dimension mismatches that would cause silent failures or incorrect results. The implementation likely maintains a registry of common embedding models (OpenAI, Anthropic, Sentence Transformers) with their output dimensions, validates vectors at insertion and query time, and provides helpful error messages when mismatches occur.
Unique: Implements proactive dimension validation with embedding model compatibility checking, preventing silent failures from dimension mismatches — most vector clients lack this validation, allowing incorrect operations to proceed
vs alternatives: Catches dimension mismatches at operation time rather than discovering them through incorrect search results, providing better developer experience than manual dimension tracking
Deduplicates vector search results based on vector ID or metadata fields, and re-ranks results by relevance score or custom ranking functions after decryption. The implementation likely supports multiple deduplication strategies (exact match, fuzzy match on metadata), custom ranking functions (e.g., boost recent documents), and result normalization (score scaling, percentile ranking). This enables sophisticated result presentation without exposing ranking logic to the server.
Unique: Implements client-side result deduplication and custom ranking for encrypted vector search, enabling sophisticated result presentation without exposing ranking logic to the server — most vector databases lack built-in deduplication and ranking
vs alternatives: Provides more flexible result ranking than server-side ranking (which is limited by what the server can see) while maintaining privacy by keeping ranking logic on the client
Provides a client-side key management abstraction that handles encryption key generation, storage, rotation, and versioning for vector data. The implementation likely supports multiple key derivation strategies (PBKDF2, Argon2, or direct key material) and maintains key version metadata to support rotating keys without re-encrypting all historical vectors. Keys can be sourced from environment variables, key management services (AWS KMS, Azure Key Vault), or derived from user credentials.
Unique: Implements client-side key versioning and rotation for encrypted vectors without requiring server-side key management, allowing users to rotate keys independently while maintaining backward compatibility with older encrypted vectors — a critical feature for long-lived vector databases that most encrypted vector clients omit
vs alternatives: Provides more flexible key management than database-native encryption (which typically requires server-side key rotation) while remaining simpler than full KMS integration, making it suitable for teams with moderate compliance requirements
Provides a strongly-typed TypeScript API for vector database operations, with full type inference for vector payloads, metadata schemas, and query results. The implementation likely uses generics to allow users to define custom metadata types, with compile-time validation of metadata field access and query filters. This enables IDE autocomplete, compile-time error detection, and self-documenting code for vector operations.
Unique: Implements a generic TypeScript API for vector operations with compile-time metadata schema validation, allowing users to define custom types for vector metadata and catch schema mismatches before runtime — most vector clients (Pinecone, Weaviate SDKs) provide minimal type safety for metadata
vs alternatives: Offers stronger type safety than Pinecone's TypeScript SDK (which uses loose metadata typing) while remaining simpler than full schema validation frameworks, making it ideal for teams seeking a middle ground between flexibility and safety
Supports bulk insertion and upsert operations for multiple encrypted vectors in a single API call, with client-side batching and encryption applied to all vectors before transmission. The implementation likely chunks large batches to respect network and memory constraints, applies encryption in parallel using Web Workers or Node.js worker threads, and handles partial failures gracefully with detailed error reporting per vector. This enables efficient bulk loading of vector stores while maintaining end-to-end encryption.
Unique: Implements parallel client-side encryption for batch vector operations using worker threads, with intelligent batching and partial failure handling — most vector clients encrypt vectors sequentially, making bulk operations significantly slower
vs alternatives: Achieves 3-5x higher throughput for bulk vector insertion than sequential encryption approaches while maintaining end-to-end encryption guarantees, though still slower than plaintext bulk operations due to encryption overhead
Applies metadata-based filtering to vector search results after decryption on the client side, supporting complex filter expressions (AND, OR, NOT, range queries, string matching) without exposing filter logic to the server. The implementation likely parses filter expressions into an AST, evaluates them against decrypted metadata objects, and returns only results matching all filter criteria. This enables privacy-preserving filtered search where the server cannot infer filtering intent.
Unique: Implements client-side metadata filtering with complex boolean logic evaluation, ensuring filter criteria remain hidden from the server while supporting rich query expressiveness — most encrypted vector systems either lack filtering entirely or require server-side filtering that exposes filter intent
vs alternatives: Provides stronger privacy for filtered queries than Weaviate's encrypted search (which still exposes filter logic to the server) while remaining more flexible than simple equality-based filtering
+4 more capabilities