agent-scan vs WMDP

Connects to live MCP servers using the MCPScanner class, retrieves tool/prompt/resource descriptions and configurations, and submits natural-language content to the Invariant analysis API for vulnerability detection. Uses a two-stage pipeline: MCP client layer establishes connections and enumerates server capabilities, then the analysis pipeline extracts and redacts sensitive data before remote submission for LLM-based threat detection.

Unique: Targets natural-language attack vectors (prompt injection, tool poisoning, toxic flows) specific to MCP infrastructure by analyzing tool descriptions and configurations rather than code; integrates with Invariant API for LLM-based semantic threat detection rather than pattern matching

vs alternatives: Detects MCP-specific supply chain attacks (cross-origin toxic flows) that generic SAST tools miss because it understands agent workflow semantics and tool composition patterns

Injects the Invariant Gateway into MCP client configurations to intercept live MCP traffic at runtime without modifying agent code. The proxy command rewrites client configuration files to route all MCP calls through a FastAPI-based mcp_scan_server that validates requests/responses against security policies before forwarding to actual MCP servers. Implements real-time policy enforcement with session-based state tracking and configurable guardrails.

Unique: Implements transparent MCP traffic interception via configuration rewriting rather than code instrumentation; uses session-based state tracking to enforce stateful policies (e.g., preventing toxic tool chains across multiple calls) and integrates Invariant Gateway for real-time semantic validation

vs alternatives: Provides runtime guardrailing without modifying agent code or MCP server implementations, enabling security policies to be deployed and updated independently of application releases

Maintains session-based state for MCP interactions in proxy mode, tracking tool calls, responses, and policy decisions across multiple requests. Stores session state in memory or external persistence layer (Redis, database) and generates comprehensive audit logs of all MCP activity. Enables stateful policy enforcement (e.g., preventing toxic tool chains) and compliance auditing.

Unique: Implements session-based state tracking with support for both in-memory and external persistence; enables stateful policy enforcement and comprehensive audit logging for compliance and incident investigation

vs alternatives: Provides built-in session state management and audit logging without requiring external logging infrastructure, enabling stateful policies and compliance auditing within the proxy

Captures and logs all MCP traffic (requests, responses, errors) for debugging and analysis. Provides detailed logging of MCP client-server interactions including payloads, timing, and error details. Supports traffic export in multiple formats (JSON, HAR) for analysis in external tools. Enables troubleshooting of MCP connectivity issues and understanding of agent behavior.

Unique: Implements comprehensive traffic capture with support for multiple export formats (JSON, HAR) and detailed timing/error information; integrates with proxy mode for transparent traffic logging without code changes

vs alternatives: Provides built-in traffic capture and debugging without requiring external packet capture tools, enabling easy analysis of MCP interactions within the scanning framework

Parses and validates MCP configuration files in JSON and YAML formats, extracting server definitions, authentication credentials, and transport protocol specifications. Validates configuration syntax and schema, detects missing required fields, and provides detailed error messages for invalid configurations. Supports multiple configuration file formats and locations (environment variables, default paths).

Unique: Implements schema-based validation for MCP configuration files with detailed error messages and support for multiple formats (JSON, YAML); integrates with configuration discovery to support multiple configuration sources

vs alternatives: Provides built-in configuration validation without requiring external schema validation tools, enabling early detection of configuration errors in CI/CD pipelines

Scans AI agent skills (packaged agent components) for embedded malware payloads, sensitive data handling violations, exposure to untrusted third parties, and hard-coded secrets using static analysis and pattern matching. Analyzes skill code, dependencies, and metadata to identify security risks before skills are integrated into agent systems. Supports both direct skill file scanning and skill registry lookups.

Unique: Combines static code analysis, signature-based malware detection, and dependency auditing specifically for agent skills; integrates with Snyk vulnerability database for known CVEs and provides skill-specific risk scoring beyond generic SAST

vs alternatives: Detects agent skill-specific risks (untrusted third-party access, sensitive data handling in skill context) that generic dependency scanners miss by understanding agent execution models and data flow patterns

Provides an offline inspect command that analyzes MCP servers and agent components locally without submitting data to remote APIs. Uses local pattern matching, heuristic analysis, and built-in vulnerability signatures to detect common security issues. Enables security-sensitive organizations to scan infrastructure without external network calls while maintaining privacy of tool descriptions and configurations.

Unique: Implements local-first vulnerability detection using built-in heuristics and pattern signatures, enabling offline scanning without external API dependencies; trades detection accuracy for privacy and network isolation

vs alternatives: Enables security scanning in restricted environments where remote API calls are prohibited, while maintaining the same CLI interface as remote scanning for operational consistency

Implements automatic data redaction in the scan analysis pipeline to remove or mask sensitive information (credentials, PII, proprietary details) before submitting tool descriptions and configurations to the Invariant analysis API. Uses configurable redaction rules and pattern matching to identify and redact secrets, API keys, email addresses, and other sensitive data. Maintains a redaction audit trail for compliance and debugging.

Unique: Integrates redaction as a first-class pipeline stage before remote submission, using configurable pattern-based rules and maintaining audit trails; enables privacy-preserving analysis without requiring separate data sanitization tools

vs alternatives: Provides built-in privacy controls within the scanning pipeline rather than requiring external data masking tools, reducing operational complexity and ensuring consistent redaction across all scan types

Evaluates LLM outputs against curated question sets spanning three distinct hazard domains (biosecurity, cybersecurity, chemical security) using domain-expert-validated benchmarks. The assessment framework maps model responses to risk levels within each domain, enabling quantitative measurement of dangerous capability presence. Responses are scored against rubrics developed by security domain experts to identify whether models can produce actionable harmful information.

Unique: Combines expert-validated questions across three distinct security domains (biosecurity, cybersecurity, chemical) into a unified benchmark framework, rather than treating each domain separately. Uses domain-expert rubrics for scoring rather than automated classifiers, ensuring nuanced assessment of harmful capability presence.

vs alternatives: More comprehensive than single-domain safety benchmarks (e.g., ToxiGen for toxicity) because it measures dangerous knowledge across multiple hazard categories simultaneously, enabling holistic safety evaluation.

Provides standardized evaluation infrastructure to measure the effectiveness of unlearning techniques (methods that remove dangerous capabilities from trained models) by comparing model performance before and after unlearning interventions. The framework isolates the impact of unlearning by holding the benchmark constant while varying the model state, enabling quantitative assessment of whether dangerous knowledge has been successfully suppressed.

Unique: Provides a standardized evaluation harness specifically designed for unlearning research, with built-in comparison logic and side-effect detection. Unlike generic benchmarks, it explicitly measures delta between model states and flags unintended capability loss.

vs alternatives: More rigorous than ad-hoc unlearning evaluation because it enforces consistent benchmark administration, statistical testing, and side-effect measurement across all methods being compared.

Implements a structured scoring framework where model responses to dangerous knowledge questions are evaluated against expert-developed rubrics that assess the degree of hazard (e.g., specificity, actionability, completeness of harmful information). Responses are scored on multi-point scales (typically 0-4 or 0-5) rather than binary pass/fail, capturing nuance in how dangerous a model's output actually is. Rubrics are domain-specific (biosecurity, cybersecurity, chemical) and developed by subject matter experts to ensure validity.

Unique: Uses domain-expert-developed multi-point rubrics rather than automated classifiers or binary labels, enabling nuanced assessment of dangerous knowledge severity. Rubrics are calibrated to distinguish between vague, incomplete, and highly actionable harmful information.

vs alternatives: More interpretable and defensible than black-box classifiers because rubric criteria are explicit and expert-validated; enables stakeholders to understand why a response received a particular score.

Analyzes patterns in how dangerous knowledge correlates across the three benchmark domains (biosecurity, cybersecurity, chemical security), identifying whether models that excel at suppressing one type of hazard tend to suppress others. The analysis uses statistical correlation and clustering techniques to reveal whether dangerous capabilities are independent or coupled in model behavior. This enables understanding of whether unlearning interventions have domain-specific or global effects.

Unique: Explicitly analyzes relationships between dangerous knowledge across domains rather than treating each domain independently. Enables discovery of whether hazards are coupled or independent in model behavior.

vs alternatives: Provides deeper insight than single-domain benchmarks by revealing how safety properties interact across different hazard categories, informing more effective unlearning strategies.

Manages the creation, validation, and versioning of benchmark questions and rubrics through a structured curation pipeline involving domain experts, adversarial testing, and iterative refinement. The pipeline ensures questions are sufficiently difficult to elicit dangerous knowledge without being unrealistic, and rubrics are calibrated through inter-rater agreement studies. Version control enables tracking of benchmark evolution and ensures reproducibility across research papers.

Unique: Implements a formal curation pipeline with expert validation and inter-rater agreement checks, rather than ad-hoc question collection. Versioning enables reproducible research and transparent tracking of benchmark evolution.

vs alternatives: More rigorous than informal benchmarks because it enforces expert review, inter-rater validation, and version control, reducing bias and enabling reproducible comparisons across papers.

Provides a unified interface for evaluating diverse LLM architectures (open-source models, API-based models, fine-tuned variants) by abstracting away implementation differences. The abstraction handles API calls (OpenAI, Anthropic, etc.), local inference (Hugging Face, Ollama), and custom model serving, enabling consistent benchmark administration across heterogeneous model types. This enables fair comparison between models with different deployment modalities.

Unique: Abstracts away differences between API-based, local, and custom-deployed models through a unified interface, enabling fair comparison without reimplementing benchmark logic for each model type.

vs alternatives: More flexible than model-specific benchmarks because it supports any LLM architecture without code changes, reducing friction for researchers evaluating new models.

Implements rigorous statistical testing to determine whether differences in dangerous knowledge scores between models or unlearning methods are statistically significant or due to random variation. Uses techniques like bootstrap confidence intervals, permutation tests, and effect size estimation to quantify uncertainty in benchmark results. This prevents overconfident claims about safety improvements that may not be robust.

Unique: Integrates formal statistical testing into the benchmark evaluation pipeline rather than relying on point estimates, ensuring claims about safety improvements are statistically justified.

vs alternatives: More rigorous than informal comparisons because it quantifies uncertainty and prevents overconfident claims about safety improvements that may not be robust to sampling variation.

Employs adversarial testing techniques to validate that benchmark questions reliably elicit dangerous knowledge and cannot be easily circumvented by prompt engineering. Red-teamers attempt to find questions that fail to elicit dangerous knowledge or rubric edge cases, and the benchmark is iteratively refined based on findings. This ensures the benchmark is robust to adversarial adaptation and captures genuine dangerous capabilities rather than surface-level patterns.

Unique: Incorporates formal red-teaming into the benchmark validation pipeline rather than assuming questions are robust, ensuring the benchmark remains effective against adversarial adaptation.

vs alternatives: More robust than static benchmarks because it actively searches for evasion techniques and iteratively refines questions, reducing the risk that models can circumvent the benchmark through prompt engineering.