Snyk vs WMDP
WMDP ranks higher at 63/100 vs Snyk at 56/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Snyk | WMDP |
|---|---|---|
| Type | Product | Benchmark |
| UnfragileRank | 56/100 | 63/100 |
| Adoption | 1 | 1 |
| Quality | 1 | 1 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Snyk Code performs deep static analysis of source code using the DeepCode AI Engine to identify security vulnerabilities, code quality issues, and anti-patterns without executing code. The engine analyzes Abstract Syntax Trees (AST) across 40+ programming languages, correlating patterns against a proprietary vulnerability database and machine learning models trained on historical vulnerability data. Real-time scanning integrates directly into IDEs, providing inline fix suggestions with contextual code examples during development.
Unique: Uses DeepCode AI Engine (proprietary machine learning models trained on historical vulnerability patterns) combined with AST-based structural analysis across 40+ languages, providing inline fix suggestions with code examples directly in the IDE rather than just flagging issues in a separate dashboard
vs alternatives: Faster developer feedback than traditional SAST tools (SonarQube, Checkmarx) because it integrates real-time scanning into the IDE with AI-generated fix examples, reducing context-switching and time-to-remediation
Snyk Open Source scans project manifests (package.json, requirements.txt, pom.xml, Gemfile, go.mod, etc.) to identify known vulnerabilities in direct and transitive open-source dependencies. The platform maintains a proprietary database of vulnerability intelligence aggregated from public CVE feeds, security advisories, and Snyk's own research. Scanning can be triggered on-demand, scheduled, or integrated into CI/CD pipelines; continuous monitoring watches for newly disclosed vulnerabilities in already-scanned projects and alerts developers to remediation paths (patches, upgrades, or workarounds).
Unique: Combines proprietary vulnerability intelligence database with continuous monitoring that automatically re-scans projects when new vulnerabilities are disclosed, providing proactive alerts rather than only scanning on-demand; includes transitive dependency analysis and remediation path recommendations (upgrade, patch, or workaround) with risk scoring
vs alternatives: More comprehensive than npm audit or pip check because it scans transitive dependencies, provides remediation recommendations with risk scoring, and continuously monitors for newly disclosed vulnerabilities rather than only scanning at build time
Snyk integrates with Jira (cloud and self-hosted) to automatically create and track vulnerability issues, enabling security findings to be managed within existing issue tracking workflows. The integration maps Snyk vulnerabilities to Jira issues with configurable fields (priority, assignee, labels, custom fields), enables developers to track remediation progress, and provides bidirectional sync to keep Snyk and Jira in sync. Integration is available in Team plan and above.
Unique: Provides bidirectional integration with Jira (cloud and self-hosted) to automatically create and track vulnerability issues with configurable field mapping, enabling security findings to be managed within existing issue tracking workflows rather than in a separate security dashboard
vs alternatives: More integrated than standalone security platforms because it brings vulnerability findings directly into Jira workflows; more flexible than native Jira security plugins because it supports multiple scanning types (code, dependencies, containers, IaC) in a unified platform
Snyk provides remediation recommendations for identified vulnerabilities, including upgrade paths for dependencies, base image recommendations for containers, and corrected IaC code examples. For open-source dependencies, Snyk can automatically apply patches via the snyk fix command or create pull requests with recommended upgrades. Recommendations are prioritized based on risk scores, and Snyk provides guidance on breaking changes and compatibility impacts to help developers make informed remediation decisions.
Unique: Provides prioritized remediation recommendations based on proprietary risk scoring, with automated patching via snyk fix command for open-source dependencies and pull request creation for dependency upgrades; includes compatibility and breaking change analysis to help developers make informed decisions
vs alternatives: More comprehensive than Dependabot or Renovate because it includes risk-based prioritization and compatibility analysis; more actionable than manual CVE research because it provides specific upgrade paths and breaking change guidance
Snyk generates compliance reports mapping vulnerability findings to regulatory frameworks (CIS benchmarks, PCI-DSS, HIPAA, SOC 2, GDPR, etc.) and provides audit trails documenting vulnerability discovery, assignment, remediation, and closure. Reports are available in multiple formats (PDF, JSON, CSV) and can be scheduled for automatic generation and delivery. Compliance reporting is available in Ignite and Enterprise plans and helps organizations demonstrate security posture to auditors and stakeholders.
Unique: Maps vulnerability findings to multiple regulatory frameworks (CIS, PCI-DSS, HIPAA, SOC 2, GDPR) and generates compliance reports with audit trails documenting discovery, assignment, and remediation; available in Ignite/Enterprise plans for organizations with strict compliance requirements
vs alternatives: More comprehensive than standalone compliance tools because it integrates vulnerability findings with compliance framework mappings; more developer-friendly than manual compliance documentation because it automates report generation and audit trail tracking
Snyk provides real-time and historical reporting capabilities designed for security engineers and GRC (Governance, Risk, Compliance) teams. Reports track vulnerability discovery trends, remediation progress, policy compliance, and security posture over time. Reporting is available in Ignite and Enterprise tiers and supports compliance documentation and executive visibility.
Unique: Provides real-time and historical reporting designed specifically for GRC teams, tracking vulnerability trends and remediation progress with compliance-focused metrics and audit trails
vs alternatives: More compliance-focused than basic vulnerability lists because it tracks trends, remediation progress, and policy compliance over time, supporting regulatory audits and executive reporting
Snyk API & Web (available as add-on) provides dynamic application security testing (DAST) capabilities for discovering and testing vulnerabilities in running APIs and web applications. The system performs active scanning of application endpoints to identify runtime vulnerabilities, injection flaws, authentication issues, and other OWASP Top 10 issues. DAST scanning complements static analysis by testing actual application behavior.
Unique: Provides dynamic application security testing (DAST) as add-on to complement static analysis, enabling runtime vulnerability discovery in APIs and web applications through active scanning
vs alternatives: Complements static analysis by testing actual application behavior at runtime, discovering vulnerabilities that static analysis cannot detect (e.g., authentication bypasses, business logic flaws)
Snyk Container scans Docker images and container registries (Docker Hub, Amazon ECR, Google Container Registry, Azure Container Registry, Artifactory, Quay, etc.) for vulnerabilities in base OS layers, application dependencies, and configuration issues. Scanning can be triggered on image push, scheduled periodically, or integrated into CI/CD pipelines. The platform analyzes image layers, identifies vulnerable packages, and provides remediation recommendations (base image upgrades, dependency patches). Integration with container registries enables continuous monitoring of deployed images for newly disclosed vulnerabilities.
Unique: Integrates with multiple container registries (Docker Hub, ECR, GCR, ACR, Artifactory, Quay) and provides continuous monitoring of deployed images for newly disclosed vulnerabilities, combined with base image recommendations and layer-by-layer vulnerability analysis rather than just flagging vulnerable packages
vs alternatives: More comprehensive than Trivy or Grype because it integrates with multiple registries, provides continuous monitoring of deployed images, and offers base image recommendations; more developer-friendly than Aqua or Twistlock because it integrates into Snyk's unified platform with consistent remediation workflows
+7 more capabilities
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.
WMDP scores higher at 63/100 vs Snyk at 56/100. Snyk leads on quality, while WMDP is stronger on ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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.