Rebuff vs code-review-graph
Side-by-side comparison to help you choose.
| Feature | Rebuff | code-review-graph |
|---|---|---|
| Type | Framework | MCP Server |
| UnfragileRank | 43/100 | 49/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Analyzes incoming prompts using fast, pattern-based rules to detect common prompt injection attack signatures (keywords, structural patterns, encoding tricks). Operates as the first defense layer before LLM-based detection, using configurable keyword lists and regex-based pattern matching to identify malicious intent without requiring model inference. Returns a heuristic score that can be compared against a configurable threshold to block suspicious inputs.
Unique: Implements defense-in-depth as first layer with configurable keyword and pattern registries, allowing teams to customize detection rules without retraining models. Uses strategy pattern to enable/disable heuristic tactics independently from other detection layers.
vs alternatives: Faster than LLM-only detection (no inference latency) and more transparent than black-box ML approaches, but less semantically sophisticated than LLM-based detection alone
Delegates prompt injection detection to a dedicated language model that analyzes user input semantically to identify malicious intent, jailbreak attempts, and instruction-override attacks. The SDK abstracts the LLM backend (OpenAI, Anthropic, local models via Ollama) and returns a detection score based on the model's confidence in identifying an attack. This layer captures sophisticated, context-aware attacks that simple heuristics miss.
Unique: Abstracts LLM provider selection via strategy pattern, supporting OpenAI, Anthropic, and local Ollama models with unified interface. Configurable thresholds per provider allow tuning sensitivity based on model capabilities and false-positive tolerance.
vs alternatives: More semantically accurate than heuristics but slower; unlike static rule-based systems, adapts to new attack patterns without code changes, though still vulnerable to adversarial prompts targeting the detection model itself
Provides APIs to log detected attacks (especially canary token leaks) to the vector database, enabling the system to learn from incidents and improve future detection. When isCanaryWordLeaked() detects a leak, the application can call logAttack() to store the attack input and metadata, which gets embedded and added to the vector database. This creates a feedback loop where each incident improves detection of similar future attacks.
Unique: Implements closed-loop learning: detected attacks (especially canary token leaks) are automatically logged to vector database, improving future detection without manual curation. Metadata logging enables forensic analysis and trend tracking.
vs alternatives: Enables continuous improvement of detection over time, unlike static rule-based or pre-trained model approaches; requires operational discipline to sanitize sensitive data before logging
Returns detailed detection results that include individual scores from each enabled tactic (heuristic score, LLM confidence, vector similarity score) alongside the final detection decision. This enables developers to understand which tactic flagged an input and why, supporting debugging, threshold tuning, and explainability to stakeholders. Detection results include metadata like matched attack patterns from vector database or heuristic rules triggered.
Unique: Returns granular per-tactic scores and metadata (matched attack patterns, heuristic rules triggered) enabling developers to understand detection decisions at multiple levels of detail. Supports both high-level flagged boolean and detailed scoring for debugging.
vs alternatives: More transparent than black-box detection systems; enables threshold tuning and debugging unavailable in opaque approaches, though requires application-level handling of detailed results
Stores embeddings of previously detected or known prompt injection attacks in a vector database (Pinecone, Supabase, or custom backends), then compares incoming prompts against this corpus using semantic similarity. When a user input's embedding exceeds a similarity threshold to known attacks, the system flags it as a potential injection. This layer learns from past incidents and enables zero-shot detection of attack variants.
Unique: Implements pluggable vector database backends (Pinecone, Supabase, custom) via abstraction layer, enabling teams to choose storage based on compliance, latency, and cost requirements. Stores attack metadata alongside embeddings for incident correlation and forensics.
vs alternatives: Learns from organizational incident history without retraining, unlike static heuristics; more scalable than maintaining curated rule lists, but requires active management of attack corpus and periodic re-embedding as threat landscape evolves
Inserts randomly generated, unique canary tokens into system prompts before sending to the LLM, then monitors the model's response to detect if those tokens appear in the output. If a canary token leaks, it indicates the model has exposed its system instructions, revealing a successful prompt injection. The SDK provides addCanaryWord() to inject tokens and isCanaryWordLeaked() to check responses, enabling post-hoc detection of instruction leakage.
Unique: Generates cryptographically random, unique canary tokens per request and provides explicit APIs (addCanaryWord, isCanaryWordLeaked) for application-level integration. Enables closed-loop learning: detected leaks can be automatically logged to vector database to improve future detection.
vs alternatives: Detects successful attacks that bypass all preventive layers; unlike purely preventive approaches, provides forensic evidence of instruction exposure and enables continuous improvement through incident-driven learning
Implements strategy pattern to compose heuristic, LLM-based, and vector database detection tactics into a unified detection pipeline. Each tactic has an independent, configurable threshold that determines sensitivity. The SDK allows enabling/disabling tactics, adjusting thresholds per tactic, and combining scores across tactics to make a final detection decision. This architecture enables teams to tune detection sensitivity for their specific risk tolerance and false-positive budget.
Unique: Uses strategy pattern to decouple detection tactics from orchestration logic, enabling runtime composition and threshold tuning without code changes. Each tactic is independently testable and can be swapped for custom implementations.
vs alternatives: More flexible than single-method detection (heuristics-only or LLM-only); allows cost-latency-accuracy tradeoffs unavailable in monolithic approaches, though requires operational discipline to tune thresholds correctly
Provides Python bindings for Rebuff detection with both sync (detect_injection) and async (async detect_injection) methods, enabling integration into synchronous Flask/Django applications and async FastAPI/Starlette services. The SDK abstracts backend configuration (LLM provider, vector database, heuristic rules) via environment variables or constructor parameters, reducing boilerplate and enabling environment-specific configuration.
Unique: Provides both sync and async APIs with unified interface, enabling drop-in integration into existing Python frameworks. Configuration abstraction via environment variables and constructor parameters allows same code to run across dev/staging/prod with different backends.
vs alternatives: More Pythonic than REST API calls; async support enables non-blocking detection in high-throughput services, unlike synchronous-only SDKs
+4 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 Rebuff at 43/100. Rebuff 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