Lakera Guard vs code-review-graph
Side-by-side comparison to help you choose.
| Feature | Lakera Guard | code-review-graph |
|---|---|---|
| Type | API | MCP Server |
| UnfragileRank | 37/100 | 49/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Analyzes user prompts and LLM inputs in real-time using a context-aware detection engine trained on the world's largest prompt injection dataset. Operates at sub-50ms latency by processing prompts through a specialized neural classifier that understands syntactic attack patterns (e.g., instruction overrides, delimiter escapes, role-play jailbreaks) while maintaining semantic context from the surrounding conversation. Returns binary classification (safe/unsafe) with confidence scores and attack type categorization.
Unique: Uses context-aware detection that analyzes prompts relative to surrounding conversation and system instructions, rather than pattern-matching in isolation. Trained on proprietary dataset claimed to be the world's largest for prompt injection attacks, enabling detection of sophisticated multi-turn jailbreaks and instruction override techniques that simpler regex or keyword-based systems miss.
vs alternatives: Achieves 3-4 orders of magnitude risk reduction vs. rule-based filters by understanding semantic intent and attack context, not just syntactic patterns, while maintaining sub-50ms latency suitable for real-time production inference.
Detects and classifies jailbreak attempts—prompts designed to override system instructions, bypass safety guidelines, or manipulate LLM behavior through role-play, hypothetical scenarios, or authority manipulation. Uses a specialized classifier trained on jailbreak patterns (e.g., 'pretend you are an unrestricted AI', 'ignore previous instructions', 'act as DAN') and returns attack type labels (role-play jailbreak, instruction override, authority manipulation, etc.) with confidence scores. Integrates into request pipeline to block or flag suspicious inputs before LLM processing.
Unique: Provides granular attack type classification (role-play jailbreak, instruction override, authority manipulation, etc.) rather than binary safe/unsafe verdict. Trained specifically on jailbreak patterns and multi-turn manipulation techniques, enabling detection of sophisticated attacks that exploit conversational context and social engineering.
vs alternatives: Outperforms generic content filters by understanding jailbreak semantics and intent, not just keyword matching, and provides attack type labels for security teams to understand threat landscape and improve system prompts accordingly.
Analyzes threats relative to surrounding conversation context, system instructions, and user role rather than in isolation. Understands that the same prompt may be benign in one context (e.g., discussing security vulnerabilities in a security training chat) but malicious in another (e.g., attempting to override system instructions in a customer service bot). Uses conversation history, system prompts, and user metadata to reduce false positives and improve detection accuracy. Enables context-aware jailbreak detection that understands multi-turn manipulation and instruction override attempts.
Unique: Analyzes threats relative to conversation context, system instructions, and user role rather than in isolation. Enables context-aware detection of sophisticated multi-turn jailbreaks and instruction override attempts that simpler pattern-matching systems miss.
vs alternatives: Reduces false positives by understanding context (e.g., legitimate security discussions vs. actual attacks) and detects sophisticated multi-turn jailbreaks that isolated prompt analysis cannot identify.
Scans user prompts and LLM outputs for exposure of sensitive personally identifiable information (PII) such as email addresses, phone numbers, credit card numbers, social security numbers, and other regulated data. Uses pattern matching combined with context-aware classification to distinguish between legitimate references (e.g., 'email me at...') and accidental leakage. Operates in real-time with sub-50ms latency and supports 100+ languages for multilingual PII detection (e.g., Portuguese and Spanish banking data formats).
Unique: Combines pattern-based detection (regex for structured PII like SSN, credit card) with context-aware classification to reduce false positives from legitimate PII references. Supports 100+ languages with language-specific pattern matching for regional data formats (e.g., Portuguese/Spanish banking identifiers), enabling compliance across global applications.
vs alternatives: Achieves lower false positive rate than simple regex-based PII detection by understanding context (e.g., distinguishing 'contact us at support@company.com' from accidental data leakage), while supporting multilingual PII detection that generic tools lack.
Detects and classifies toxic, abusive, hateful, or otherwise harmful language in user prompts and LLM outputs using a trained classifier. Analyzes text for profanity, hate speech, threats, harassment, and other harmful content categories. Operates in real-time with sub-50ms latency and supports 100+ languages. Returns binary classification (toxic/non-toxic) with content category labels and confidence scores, enabling applications to block, flag, or quarantine harmful inputs before LLM processing.
Unique: Provides granular content category classification (profanity, hate speech, threats, harassment) rather than binary toxic/non-toxic verdict. Supports 100+ languages with language-specific toxic content patterns, enabling moderation across global applications with culturally-aware detection.
vs alternatives: Outperforms generic profanity filters by understanding context and intent, not just keyword matching, and provides category labels for moderation workflows. Multilingual support enables consistent content moderation across diverse user bases and languages.
Provides a single, unified API endpoint for detecting multiple threat types (prompt injection, jailbreaks, PII leakage, toxic content) across any LLM application, regardless of which underlying LLM model is used (OpenAI, Anthropic, open-source models, etc.). Operates as a middleware layer that intercepts requests before LLM inference and responses after generation, enabling consistent security posture across heterogeneous model deployments. Abstracts threat detection logic from model-specific implementations, allowing teams to swap LLM providers without reconfiguring security rules.
Unique: Provides a single, model-agnostic API that detects threats across any LLM provider or model, abstracting threat detection from model-specific implementations. Enables teams to swap LLM providers (OpenAI to Anthropic, proprietary to open-source) without reconfiguring security rules or threat detection logic.
vs alternatives: Decouples security from model choice, enabling flexible LLM provider selection and migration without security rework. Simpler than building model-specific threat detection for each provider or maintaining separate security pipelines per model.
Executes threat detection (prompt injection, jailbreaks, PII, toxic content) with sub-50ms latency, enabling integration into real-time LLM inference pipelines without significant performance degradation. Achieves low latency through optimized neural classifiers, efficient tokenization, and cloud-native deployment with geographic distribution. Designed for production deployments handling hundreds of prompts per second with minimal added latency to user-facing LLM applications.
Unique: Optimizes threat detection for real-time inference pipelines through specialized neural classifiers and cloud-native deployment, achieving sub-50ms latency suitable for production LLM applications. Designed to scale from zero to hundreds of prompts per second without significant latency degradation.
vs alternatives: Faster than local threat detection models (which require model loading and inference) and more responsive than batch processing, enabling real-time threat detection in user-facing LLM applications without noticeable latency impact.
Automatically scales threat detection capacity from zero to hundreds of prompts per second using cloud-native infrastructure and elastic resource allocation. Handles traffic spikes and variable load without manual scaling configuration or capacity planning. Designed for production deployments where threat detection must keep pace with LLM inference throughput without becoming a bottleneck. Manages concurrent requests, queuing, and resource allocation transparently to the client.
Unique: Provides automatic elastic scaling from zero to hundreds of prompts per second without manual capacity planning or infrastructure management. Cloud-native architecture abstracts scaling complexity from the client, enabling threat detection to scale transparently with LLM traffic.
vs alternatives: Eliminates capacity planning overhead compared to self-hosted threat detection models, and avoids bottlenecks that occur when threat detection throughput lags behind LLM inference capacity.
+3 more capabilities
Parses source code using Tree-sitter AST parsing across 40+ languages, extracting structural entities (functions, classes, types, imports) and storing them in a persistent knowledge graph. Tracks file changes via SHA-256 hashing to enable incremental updates—only re-parsing modified files rather than rescanning the entire codebase on each invocation. The parser system maintains a directed graph of code entities and their relationships (CALLS, IMPORTS_FROM, INHERITS, CONTAINS, TESTED_BY, DEPENDS_ON) without requiring full re-indexing.
Unique: Uses Tree-sitter AST parsing with SHA-256 incremental tracking instead of regex or line-based analysis, enabling structural awareness across 40+ languages while avoiding redundant re-parsing of unchanged files. The incremental update system (diagram 4) tracks file hashes to determine which entities need re-extraction, reducing indexing time from O(n) to O(delta) for large codebases.
vs alternatives: Faster and more accurate than LSP-based indexing for offline analysis because it maintains a persistent graph that survives session boundaries and doesn't require a running language server per language.
When a file changes, the system traces the directed graph to identify all potentially affected code entities—callers, dependents, inheritors, and tests. This 'blast radius' computation uses graph traversal algorithms (BFS/DFS) to walk the CALLS, IMPORTS_FROM, INHERITS, DEPENDS_ON, and TESTED_BY edges, producing a minimal set of files and functions that Claude must review. The system excludes irrelevant files from context, reducing token consumption by 6.8x to 49x depending on repository structure and change scope.
Unique: Implements graph-based blast radius computation (diagram 3) that traces structural dependencies to identify affected code, rather than heuristic-based approaches like 'files in the same directory' or 'files modified in the same commit'. The system achieves 49x token reduction on monorepos by excluding 27,000+ irrelevant files from review context.
code-review-graph scores higher at 49/100 vs Lakera Guard at 37/100. Lakera Guard leads on adoption, while code-review-graph is stronger on quality and ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: More precise than git-based impact analysis (which only tracks file co-modification history) because it understands actual code dependencies and can exclude files that changed together but don't affect each other.
Includes an automated evaluation framework (`code-review-graph eval --all`) that benchmarks the tool against real open-source repositories, measuring token reduction, impact analysis accuracy, and query performance. The framework compares naive full-file context inclusion against graph-optimized context, reporting metrics like average token reduction (8.2x across tested repos, up to 49x on monorepos), precision/recall of blast radius analysis, and query latency. Results are aggregated and visualized in benchmark reports, enabling teams to understand the expected token savings for their codebase.
Unique: Includes an automated evaluation framework that benchmarks token reduction against real open-source repositories, reporting metrics like 8.2x average reduction and up to 49x on monorepos. The framework enables teams to understand expected cost savings and validate tool performance on their specific codebase.
vs alternatives: More rigorous than anecdotal claims because it provides quantified metrics from real repositories and enables teams to measure performance on their own code, rather than relying on vendor claims.
Persists the knowledge graph to a local SQLite database, enabling the graph to survive across sessions and be queried without re-parsing the entire codebase. The storage layer maintains tables for nodes (entities), edges (relationships), and metadata, with indexes optimized for common query patterns (entity lookup, relationship traversal, impact analysis). The SQLite backend is lightweight, requires no external services, and supports concurrent read access, making it suitable for local development workflows and CI/CD integration.
Unique: Uses SQLite as a lightweight, zero-configuration graph storage backend with indexes optimized for common query patterns (entity lookup, relationship traversal, impact analysis). The storage layer supports concurrent read access and requires no external services.
vs alternatives: Simpler than cloud-based graph databases (Neo4j, ArangoDB) because it requires no external services or configuration, making it suitable for local development and CI/CD pipelines.
Exposes the knowledge graph as an MCP (Model Context Protocol) server that Claude Code and other LLM assistants can query via standardized tool calls. The MCP server implements a set of tools (graph management, query, impact analysis, review context, semantic search, utility, and advanced analysis tools) that allow Claude to request only the relevant code context for a task instead of re-reading entire files. Integration is bidirectional: Claude sends queries (e.g., 'what functions call this one?'), and the MCP server returns structured graph results that fit within token budgets.
Unique: Implements MCP server with a comprehensive tool suite (graph management, query, impact analysis, review context, semantic search, utility, and advanced analysis tools) that allows Claude to query the knowledge graph directly rather than relying on manual context injection. The MCP integration is bidirectional—Claude can request specific code context and receive only what's needed.
vs alternatives: More efficient than context injection (copy-pasting code into Claude) because the MCP server can return only the relevant subgraph, and Claude can make follow-up queries without re-reading the entire codebase.
Generates embeddings for code entities (functions, classes, documentation) and stores them in a vector index, enabling semantic search queries like 'find functions that handle authentication' or 'locate all database connection logic'. The system uses embedding models (likely OpenAI or similar) to convert code and natural language queries into vector space, then performs similarity search to retrieve relevant code entities without requiring exact keyword matches. Results are ranked by semantic relevance and integrated into the MCP tool suite for Claude to query.
Unique: Integrates semantic search into the MCP tool suite, allowing Claude to discover code by meaning rather than keyword matching. The system generates embeddings for code entities and maintains a vector index that supports similarity queries, enabling Claude to find related code patterns without explicit keyword searches.
vs alternatives: More effective than regex or keyword-based search for discovering related code patterns because it understands semantic relationships (e.g., 'authentication' and 'login' are related even if they don't share keywords).
Monitors the filesystem for code changes (via file watchers or git hooks) and automatically triggers incremental graph updates without manual intervention. When files are modified, the system detects changes via SHA-256 hashing, re-parses only affected files, and updates the knowledge graph in real-time. Auto-update hooks integrate with git workflows (pre-commit, post-commit) to keep the graph synchronized with the working directory, ensuring Claude always has current structural information.
Unique: Implements filesystem-level watch mode with git hook integration (diagram 4) that automatically triggers incremental graph updates without manual intervention. The system uses SHA-256 change detection to identify modified files and re-parses only those files, keeping the graph synchronized in real-time.
vs alternatives: More convenient than manual graph rebuild commands because it runs continuously in the background and integrates with git workflows, ensuring the graph is always current without developer action.
Generates concise, token-optimized summaries of code changes and their context by combining blast radius analysis with semantic search. Instead of sending entire files to Claude, the system produces structured summaries that include: changed code snippets, affected functions/classes, test coverage, and related code patterns. The summaries are designed to fit within Claude's context window while providing sufficient information for accurate code review, achieving 6.8x to 49x token reduction compared to naive full-file inclusion.
Unique: Combines blast radius analysis with semantic search to generate token-optimized code review context that includes changed code, affected entities, and related patterns. The system achieves 6.8x to 49x token reduction by excluding irrelevant files and providing structured summaries instead of full-file context.
vs alternatives: More efficient than sending entire changed files to Claude because it uses graph-based impact analysis to identify only the relevant code and semantic search to find related patterns, resulting in significantly lower token consumption.
+4 more capabilities