All Snyk resources

Framework for adversarial AI testing that organizes security strategy around three distinct lenses: DAST to map the exposed surface, AI penetration testing to validate exploitability, and AI red teaming to demonstrate business impact. This approach advocates for a unified testing harness over isolated vendor engagements, emphasizing orchestration to bridge visibility gaps and chain risks effectively. The framework addresses known-taxonomy chains and cross-layer behavioral emergence by sharing data and primitives between DAST, AI pentesting, and red team exercises.

Library for benchmarking secure-and-functional vulnerability remediation, evaluating frontier models like Gemini 3.1 Pro and Claude Opus 4.6. Snyk Agent Fix, augmented with Snyk Intelligence, demonstrably lifts fix rates by over 14%, improving performance from 74.6% to 85.4% for Opus 4.6. This enhancement is most pronounced in areas where models struggle, such as Python samples, where Snyk Intelligence boosts fixes from 64% to 88%.

Library integrating Snyk Studio with Snowflake Cortex Code provides real-time scanning of AI-generated Python code, dependency analysis for Snowpark Container Services, and direct security feedback to developers. This "secure at inception" approach addresses vulnerabilities in application logic, including injection flaws in dynamic SQL, and mitigates risks from third-party packages and poisoned dependencies. The integration aims to accelerate secure innovation, eliminate shadow AI risks, reduce costs by catching bugs early, and offer unified governance for Snowflake application development.

Library of tests for Snyk VulnBench JS 1.0, evaluating LLM security review repeatability by measuring consistency of findings on JavaScript code. The benchmark uses small Express applications and compares LLM outputs against Snyk Code SAST reference vulnerabilities, revealing that LLM-generated findings are inconsistently repeatable, particularly those outside the established reference set, unlike the deterministic output of traditional SAST tools.

Analysis of nearly 10,000 developer environments reveals significant risks in agentic development, with 43% of developers using multiple AI coding tools and 50.8% employing MCP servers. These configurations, along with agent skills (used by 22.8%), introduce a new supply chain layer where prompt injection findings and malicious code patterns are already present in active tooling. Traditional AppSec controls need expansion to govern these developer environments, as risks emerge before code commitment, impacting tools like Claude, Cursor, and VS Code extensions.

Library for Agentic Development Security (ADS) that secures AI-driven software development by embedding security directly into workflows. It provides visibility, governance, and control over tools agents use, their actions, and generated code. ADS addresses risks from dynamic, multi-step workflows and autonomous agent behavior, moving security from post-code analysis to real-time evaluation of agent supply chains, behavior, and generated code, preventing issues like the deletion of a production database by an AI agent with improper credentials and no guardrails.

Integration that brings Snyk package vulnerability intelligence to SBOMs stored in ServiceNow. This tool identifies vulnerability severity within application and software package contexts, offering fix information and remediation guidance. It automates the creation of application vulnerable items in ServiceNow Vulnerability Response, enabling task assignment, progress tracking, and stakeholder notification. The integration aids in managing risks from zero-day vulnerabilities, such as recent http2 or curl vulnerabilities, and facilitates the analysis of third-party SBOMs for pre-deployment vulnerability assessment.

Writeup detailing WebExtension security vulnerabilities discovered in React Developer Tools (CVE-2023-5654) and Vue.js devtools (CVE-2023-5718). The research highlights risks such as unverified external messages allowing arbitrary URL fetching via `fetch()`, and unauthorized access to page capture APIs enabling sensitive data leakage through base64 encoded screenshots. The analysis explores the WebExtension architecture and specific pitfalls leading to these findings.

Library integrating Nightfall AI's advanced secrets scanning with Snyk's developer security platform. This partnership enables automated detection of PII, PHI, PCI, API keys, and other sensitive data across cloud environments and AI-generated code. Nightfall's AI-native capabilities offer over 100 data type detectors, context-aware alerts to reduce false positives, and developer-centric remediation, enhancing Snyk's ability to secure the software supply chain and application security posture.

Guide on securing JSON Web Tokens (JWTs) detailing three core best practices. It emphasizes keeping JWTs secret through HTTPS, HttpOnly/Secure cookie flags, and secure browser storage, while highlighting the risks of XSS. The guide stresses the importance of robust JWT validation, including signature verification, and checking claims like expiration, issuer, and audience. It also advocates for setting expiration times on JWTs to limit their usability and prevent unauthorized access. The article mentions tools like Snyk for identifying vulnerabilities and libraries such as Flask-JWT-Extended and PyJWT for implementation.

Library integrating a shift-left approach to software supply chain security, utilizing Snyk Code and APIs for proactive vulnerability detection and SBOM generation. Kroger’s implementation emphasizes developer efficiency and risk management, with features that scan pull requests, alert on suspicious package downloads, and automate compliance for PCI DSS 4.0 requirements. The platform supports the company’s efforts in navigating complex technology stacks and addressing open-source dependency risks.

Library for building a Node.js security scanning API using Platformatic and Fastify. This resource details how to scaffold a Node.js service with Platformatic, integrate the Snyk CLI and API for vulnerability detection, and create a POST endpoint to test npm packages. It emphasizes securing API tokens using environment variables and IDE extensions like the Snyk VS Code extension for secret detection.

Talk from Snyk and Dynatrace AI experts highlights three key takeaways for secure generative AI adoption. Prioritizing AI governance, involving cross-functional teams for ethics, request ingestion, and communication is crucial. Taking a patient and considered approach to new technologies is advised, with a focus on understanding data flow, explainability, and transparency in AI tools. Finally, balancing AI opportunities with risks in development necessitates rigorous code security practices, including threat management scanning and clear documentation of AI-generated code, to avoid vulnerabilities and data overexposure.

Library that integrates with AI-assisted development to address risks from tools like GitHub Copilot and Amazon CodeWhisperer. It scans AI-generated code in real-time within the IDE, flagging vulnerabilities stemming from bad training data or hallucinations, and provides quick fixes. The library is presented as a method to verify code against known standards, ensuring trust and security are embedded throughout the SDLC, much like safety measures on a construction site.

Library that categorizes AI developer tools, highlighting Security Companions like Snyk Code for real-time analysis of AI-generated and developer-written code. It also details Coding Assistants (GitHub Copilot, Amazon CodeWhisperer), Chatbots/LLMs (ChatGPT, Claude.AI), AI code search (Sourcegraph, Phind), and AI code testing (Codium), emphasizing the need to pair coding assistants with security tools.

Library that uses DeepCode AI, a hybrid approach combining symbolic and machine learning AI, to secure AI-generated code. This technology analyzes code in real-time within the IDE, identifying vulnerabilities like those introduced by tools such as Copilot. It provides accurate results with reduced false positives by incorporating multi-file, interfile, and dataflow analysis, and offers AI-generated fix candidates that are validated for security before recommendation, mitigating risks of license infringement, IP violations, and software vulnerabilities.

Guidance on securing SBOMs details NSA recommendations for open source software management, secure repository creation, and crisis management. Practices include evaluating OSS, risk assessment, maintaining internal repositories, vulnerability response, and creating validated SBOMs with details on components, versions, and licenses. Snyk integrates with Google Cloud services like CloudBuild, Artifact Registry, and GKE to help users find and fix vulnerabilities, scan containers, and generate enriched SBOMs.

Library integrating Snyk's security expertise with Google Gemini's AI coding assistance. This partnership delivers automated fixes and in-line security feedback for AI-generated code within IDEs like Google Cloud Code, providing full application context to identify vulnerabilities early. It leverages DeepCode AI for SAST and aims to accelerate development velocity without compromising security, allowing teams to adopt AI coding assistants confidently.

Talk from DevOpsDays Singapore 2024 highlights security challenges in AI-generated code. Tools like Copilot, AWS Code Whisperer, and Gemini can increase development speed but may introduce vulnerabilities such as SQL injection and XSS, or use outdated libraries. An analysis showed 40% of Copilot-generated code had flaws. Live demonstrations illustrated how AI can both introduce and help fix these security issues with proper prompting. Security tools like Snyk, integrating into development environments, are crucial for scanning and remediating these vulnerabilities early in the SDLC.

Library that enhances LLM performance for security vulnerability autofixing. It employs proprietary CodeReduce technology, which utilizes program analysis to narrow the LLM's attention to critical code snippets, significantly improving fix generation accuracy and speed. This approach addresses LLM limitations by focusing on curated security fix datasets and contextual code, outperforming existing models like GPT-4 on various vulnerability types including AST, Local, FileWide, SecurityLocal, and SecurityFlow issues.

Survey of 459 IT professionals globally, including AppSec, developers, and C-suite, reveals that while organizations feel ready for generative AI coding tools, less than 20% conduct formal POCs. Security fears are the biggest adoption barrier, yet AppSec teams express greater concern about AI code security and insufficient policies compared to management. The report highlights a discrepancy in AI readiness perceptions across roles, with leadership being more optimistic than those directly involved with code.

Talk from Black Hat Asia featuring experts from Trust Bank and TASConnect, discussing strategies for securing next-generation applications. It highlights the challenges posed by complex architectures, AI-generated code (like that from GitHub Copilot and Google Gemini), and multi-cloud deployments. The session emphasizes a proactive, developer-first approach, leveraging tools such as Snyk for immediate feedback and risk prioritization, and tracking key metrics like security training implementation and time to remediate to align security with business goals.

Platform offering open source maintainers free access to the Snyk AI Security Platform. It focuses on issue prioritization using exploitability, reachability, and asset criticality, alongside automated fix pull requests for vulnerable dependencies via the Snyk Remediation Agent, which uses frontier-model reasoning for validated, merge-ready fixes in Snyk Open Source and Snyk Code.

Library updates for Snyk Code now secure AI builds by tracking data flows from LLM libraries, including those from OpenAI, HuggingFace, Anthropic, and Google. This enhancement performs taint analysis on data returned from LLM sources, treating it as untrusted by default, to detect vulnerabilities such as prompt injections, SQL injection, and XSS. The tool identifies unsanitized data sinking into sensitive functions or data stores, alerting users to potential security issues even in projects with initially unsupported LLM frameworks.

Library for scanning Node.js projects for exposed hardcoded secrets and credentials, demonstrated with the Snyk goof project. It utilizes Snyk to identify vulnerabilities and then refactors code to leverage Doppler for centralized secret management, preventing secrets from being pushed to GitHub and enabling environment-specific configurations. The process involves adding secrets to Doppler and updating application code to use environment variables injected via the Doppler CLI.

Library for AI-driven application security, Snyk Agent Fix integrates into IDEs for real-time autofixing, leveraging a specialized, proprietary LLM trained on security data rather than general code functionality. This approach ensures more reliable security fixes than those from general-purpose LLMs. The library aims for accuracy and speed, catering to both developer and security teams, and includes features like SBOM generation and proactive risk neutralization, as demonstrated by its early discovery of the Leaky Vessels zero-day vulnerability.

Library provides a free learning path on Snyk Learn, focusing on the OWASP Top 10 for LLMs and GenAI. This path addresses critical security challenges in AI systems, including prompt injection and sensitive information disclosure, equipping developers and security professionals with knowledge to secure AI-driven applications.

Library for continuous vulnerability management that integrates Snyk DeepCode AI into IDEs like VS Code and IntelliJ IDEA. It proactively identifies security issues in open-source components, AI-generated code (e.g., from GitHub Copilot), and containerized applications, offering real-time feedback and mitigations for vulnerabilities like XSS and SQL injection, thereby enhancing application security throughout the development lifecycle.

Program that partners with AI coding assistants like Codieum, Tabnine, TabbyML, and Qodo to integrate Snyk's security capabilities. This initiative addresses vulnerabilities common in AI-generated code, such as inaccuracies, hallucinations, security flaws, data poisoning, and prompt injections. By embedding real-time SAST guardrails and automated remediation through DeepCode AI Fix into IDE extensions, the program aims to secure AI-assisted development workflows and increase trust in AI-generated software.

Library for automatically fixing common security vulnerabilities, such as Cross-site Scripting (XSS) in Java applications, by leveraging a hybrid AI model. This tool, integrated into IDEs, goes beyond providing remediation advice by directly applying secure code fixes, exemplified in a Spring Boot application using the Thymeleaf template engine and the faker library. Unlike generative AI assistants that may introduce insecure code, this library uses a combination of generative AI, symbolic AI, and machine learning, trained on curated security research data, to ensure secure code generation.

Library for AI-powered application security, Snyk Code, offers automated detection and remediation of vulnerabilities. Utilizing DeepCode AI, it scans code directly within the IDE, providing rapid SAST analysis and auto-fixing suggestions via its Snyk AgentFix feature. This multi-model AI approach combines different methodologies for enhanced reliability, ensuring suggested fixes are screened by symbolic AI before developer application, reducing mean-time-to-remediate and improving code security for AI-generated code.

Writeup detailing the compromise of the `@lottiefiles/lottie-player` npm package, which injected malicious code into versions 2.0.5 through 2.0.7, enabling cryptocurrency wallet theft. The incident highlights supply chain risks, particularly when using CDNs without pinned dependency versions, and details how tools like Snyk can identify vulnerable installations of this and similar packages.

Library extending developer security, Snyk has acquired Probely, a provider of API Security Testing and Dynamic Application Security Testing (DAST). This acquisition integrates modern DAST capabilities, focusing on a developer-first approach with low false positive rates, simple implementation, and CI/CD pipeline integration, addressing the growing need for API security in the GenAI era and providing enhanced visibility into application risk.

Ebook detailing how to secure GenAI development with Snyk. It addresses the risks of AI-generated code, where up to 40% from tools like Copilot can contain security flaws, and the speed at which CVEs can be exploited. The ebook explains how Snyk Code, with its DeepCode AI and Snyk Agent Fix, offers in-IDE security, real-time scanning, AI-driven auto-fixing, and faster scans compared to traditional solutions, balancing productivity with robust security for both human-written and AI-generated code.

Analysis of Cursor.com AI Code Editor by Snyk Security Labs details testing for dependency confusion vulnerabilities. Researchers uploaded intentionally named packages to the public NPM repository to test if Cursor's build system would mistakenly pull private extensions from the public registry. These packages exfiltrated system details such as username, hostname, and environmental variables to confirm installation. The analysis concluded that Cursor was not vulnerable to dependency confusion and no sensitive data was disclosed.

Library for generating Software Bills of Materials (SBOMs) using the Snyk CLI. It supports various programming languages and package managers, creating SBOMs in CycloneDX, XML, or SPDX formats. The Snyk CLI can also scan existing SBOM files for known vulnerabilities and integrates with tools like Bomber for analysis. Automating SBOM generation within CI/CD pipelines is crucial for security, compliance, and transparency.

Integration between Snyk and Google Security Command Center consolidates application and cloud security findings into a single view. This allows CISOs and security teams to monitor Snyk-detected application vulnerabilities, such as critical issues in open-source libraries within container images, alongside cloud security issues identified by Security Command Center. The integration streamlines risk management, enables near real-time detection, and prioritizes remediation by providing actionable advice from Snyk alongside Google's threat intelligence and Mandiant's expertise.

Library that leverages Symbolic AI for static application security testing (SAST). Snyk Code analyzes code paths to detect vulnerabilities, outperforming traditional pattern-matching methods. It successfully identified open redirects in Python Flask applications, and Server-Side Request Forgery (SSRF), Cross-Site Scripting (XSS), and CRLF injection in Node.js Express applications. Snyk Code not only detects these issues but also offers suggested fixes and contextual best practices.

Library that analyzes Node.js applications for insecure JSON Web Token (JWT) usage, specifically identifying the misuse of `jsonwebtoken`'s `jwt.decode()` function, which can lead to broken authentication vulnerabilities. It also flags hardcoded secrets, insufficient logging, lack of rate limiting, and missing token expiration enforcement.

Learning path from Snyk Learn addresses the OWASP Top 10 for API security risks. This interactive resource provides developers and security teams with a deep understanding of critical API vulnerabilities and actionable strategies for mitigation. It covers real-world scenarios to fortify API security, complementing existing learning paths on OWASP Top 10 and GenAI & LLM risks.

Library contribution that fixes CVE-2025-22869, a denial-of-service vulnerability in golang.org/x/crypto/ssh, within the Golang Bento project. Snyk proactively addressed the issue by updating dependencies to secure versions, demonstrating their commitment to securing open-source projects through their Secure Developer Program.

Reference for leveraging ServiceNow Vulnerability Assignment rules to automate the routing of application vulnerabilities. The integration correlates vulnerability data with Configuration Management Database (CMDB) fields, enabling automatic assignment to the responsible developer teams based on application tags. This streamlines remediation workflows and enhances visibility for application security teams and CISOs.

Library for the Snyk VS Code extension that enhances developer workflows by introducing "delta findings." This feature allows developers to see only security issues introduced by their current code changes, filtering out noise from the base branch. It supports proactive remediation of vulnerabilities like command injection and cross-site scripting in JavaScript and Python code. The extension integrates with Snyk Code, Snyk Open Source, and Snyk IaC, offering AI-powered auto-fixing and granular filtering by severity and source.

Fireside chat slides from CIBC detailing application security challenges in financial services, including balancing speed with compliance mandates like PCI DSS and SOC 2, the role of AI-driven tools in vulnerability management, and fostering a developer-first security culture. The discussion highlights the importance of continuous monitoring, automation, and developer education to strengthen security posture against cybercrime and data privacy concerns in legacy and modern applications.

Library integration enables developers to scan code for SAST, SCA, and IaC vulnerabilities directly within Google Cloud's Gemini Code Assist using natural language prompts. This streamlines the application security process by consolidating security checks within the IDE, reducing context switching and accelerating vulnerability detection and remediation with features like Snyk Agent Fix. The partnership allows for secure AI adoption at scale, providing enhanced developer experience and visibility into security risks across the software development lifecycle.

Library for AI-driven dynamic application security testing (DAST), Snyk API & Web, redefines security for modern, AI-powered applications. Integrating Probely's DAST technology into its platform, it addresses complex APIs and Large Language Model (LLM) interactions, expanding coverage for OWASP Top 10 risks like BOLA. The solution offers holistic risk views, developer-first workflows, and synergistic detection by correlating DAST with SAST findings. Its roadmap includes AI-driven API testing and code-informed dynamic testing for enhanced vulnerability management.

Library integration between Snyk and ServiceNow streamlines application security for AI-generated code. This partnership offers developers real-time vulnerability detection and automated fixes for AI-generated code, custom code, and dependencies, reducing mean time to remediate by 84% with Snyk DeepCode AI. ServiceNow's workflows automate task assignment and tracking for AppSec teams, providing broad risk visibility and enabling efficient remediation.

Library updates from RSAC 2025 highlight Snyk's advancements in application security, particularly concerning generative AI. The conference featured discussions on AI's impact on software security and Snyk's role in developing secure AI. Snyk API & Web received a Global InfoSec Award for Most Innovative API Security and was recognized as one of CRN’s 20 Coolest New Cybersecurity Products. Snyk also joined the Coalition for Secure AI (CoSAI) to promote secure and ethical generative AI development. The company also hosted a "Women Leading Security" panel to foster diversity and inclusivity in the industry.

Platform that secures AI-powered development by integrating security directly into AI workflows. It introduces Snyk Assist for real-time secure coding guidance, Snyk Agent Fix for autonomous code remediation, Snyk Studio for integrating security into AI development platforms, and Snyk Guard for adaptive policy enforcement. The platform enhances visibility across AI applications, including API and web app security with AI-powered BOLA detection, and provides intelligent prioritization for identified vulnerabilities.

Library for AI-native security, Snyk Labs offers research, technical demos, and prototypes tackling new threats from LLMs and agents. It focuses on AI Security Posture Management (AI-SPM) and developing an AI Bill of Materials (AI BoM), alongside a GenAI Model Risk Registry. Snyk Labs also contributes to LLM security standards with OWASP and participates in initiatives like CoSAI.

Analysis of AI-driven development security risks, including vulnerabilities in AI-generated code (48% of snippets) and issues from open-source AI tools (32% of developers face problems). This piece highlights package hallucination (over 5% of commercial AI code containing fake packages) and emphasizes the need for evolving security practices beyond traditional "shift left" methods to address the accelerated pace and increased risk introduced by AI coding assistants, advocating for the Snyk AI Trust Platform.

Library for AI-powered vulnerability remediation that blends hybrid AI with rigorous validation. Snyk Agent Fix combines a fine-tuned LLM with static analysis, including DeepCode AI and CodeReduce, to generate and verify fixes before presenting them to developers, achieving over 80% fix accuracy. It keeps customer code private and offers configurable policies for enterprise-grade security guardrails.

Tool for achieving HIPAA compliance in application development, Snyk API & Web automates security scanning to identify vulnerabilities like SQL Injection and Cross-Site Scripting (XSS). It supports continuous security testing, encryption, access controls, and audit trails, essential for protecting electronic protected health information (ePHI) and meeting regulatory requirements.

Library for integrating security into AI coding workflows, focusing on "guardrails" over restrictions. It highlights Snyk's capabilities, including pull request checks, IDE plugins for real-time scanning and auto-remediation, and conditional access controls via endpoint management tools like Microsoft Intune and Jamf, ensuring secure AI code generation from the start. The approach emphasizes developer-friendly adoption through incentives and education, rather than strict mandates.

Library for AI-assisted application security, Snyk Code integrates SAST with Snyk Agent Fix, an AI-powered auto-remediation assistant. Snyk Agent Fix generates and validates code fixes autonomously within IDEs and pull requests, boasting 80% accuracy with patented CodeReduce technology. This reduces manual remediation time from hours to seconds, ensuring human- and AI-generated code is secured early in the development lifecycle, with enhanced visibility, prioritization, and policy enforcement capabilities.

Library for real-time vulnerability detection within Cursor, leveraging Snyk's CLI MCP server for zero-setup interoperability with AI-generated code. This integration enables immediate identification of security issues in code and open-source packages as they are generated, facilitating agile development cycles without compromising security.

Library introducing three free capabilities to secure AI-native development: Snyk's MCP Server for embedding security into agentic workflows with tools like Cursor and Co-Pilot, Snyk AI-BOM for discovering and governing AI components, and Toxic Flow Analysis (TFA) for detecting novel attack vectors in AI applications, addressing vulnerabilities like prompt injections and MCP rug pulls.

Library integration enhancing API discovery by ingesting API inventories and schemas directly from Akamai. This automates the process of providing API specifications for DAST scanning, overcoming the challenge of manual schema management for Snyk customers. Akamai's discovery capabilities, spanning traffic analysis and source code inspection, feed comprehensive data into Snyk API & Web, enabling single-click API scanning and increased coverage. This empowers joint customers to proactively test and remediate API vulnerabilities.

Framework for AI readiness, developed by Snyk, offers five pillars: visibility, ownership, secure design, cultural enablement, and continuous assurance. This framework addresses the evolving landscape of AI-powered systems, emphasizing shared accountability and the need for integrated security throughout the development lifecycle. Sessions at Snyk's Lighthouse event highlighted practical strategies for agentic app development, real-time risk modeling, and upskilling teams, with live demonstrations showcasing how tools like Cursor and Snyk can reduce security fix times from hours to minutes. The framework aims to accelerate responsible AI adoption by building trust and implementing smart guardrails from the outset.

Platform for securing AI development by federal agencies, Snyk helps teams meet new mandates by identifying and fixing vulnerabilities at scale. It integrates with existing workflows and provides credible vulnerability intelligence sourced from CVE and CWE. Key features include Snyk Agent Fix for automated remediation, Snyk Assist for real-time developer support, and support for AIBOMs and Toxic Flow Analysis to address complex AI-specific vulnerabilities like indirect prompt injection.

Library for agentic container security, Snyk MCP Server simplifies vulnerability scanning by integrating with AI coding assistants and IDEs. This tool addresses container security challenges by enabling "shift left" practices, allowing developers to identify and fix Common Vulnerabilities and Exposures (CVEs) and operating system vulnerabilities before committing code. Snyk MCP Server supports scanning container images for OS vulnerabilities and vulnerable open-source application packages, and can be configured within tools like Cursor and Copilot.

Library view in Snyk's UI prioritizes open source vulnerability remediation by grouping issues by dependency. This new default view allows users to evaluate the holistic impact of library upgrades, moving beyond individual vulnerability fixes. It aids in making informed decisions by presenting a cost/benefit analysis of resolving multiple vulnerabilities with a single version update, supporting workflows for Maven, .NET, npm, Python, Ruby, and Yarn projects.

Platform features enhance AI workflow security by providing governance and visibility. The Ignore Approval Workflow allows developers to request findings bypass while AppSec teams maintain oversight. CLI Upload persists local scan results for comprehensive risk tracking. Group by Dependency View aids vulnerability prioritization by consolidating issues per dependency. The Export API integrates Snyk data into internal systems for reporting, and Agent Usage to IDE reports track AI coding assistant utilization across environments. New Snyk Learn Reports measure developer education program effectiveness.

Library integrations, Snyk for Devin and Snyk for Windsurf, embed Snyk Studio's security intelligence into Cognition's AI-native developer tools. These integrations offer real-time security scanning within development workflows, enabling accelerated agent-led remediation by AI agents like Devin and Windsurf. This partnership aims to scale security processes alongside AI-driven development velocity, reducing security backlogs and improving resource allocation by automating routine fixes.

Library: Evo by Snyk is the world's first Agentic Security Orchestrator, designed to address the evolving security challenges posed by AI-native systems. It proactively defends against threats like model supply chain attacks, memory abuse, prompt manipulation, and agent escalation. Evo observes AI attack surfaces, orients risk with contextual intelligence, and automatically enacts response plans using specialized AI security agents. This approach aims to integrate security seamlessly into AI development, enabling innovation without compromising safety.

Library for securing AI-driven development, Snyk Studio now empowers all customers with enterprise-wide rollout capabilities via Jamf and a VS Code extension. It embeds security intelligence directly into AI coding assistants like Cursor and Copilot, enabling "Secure at Inception" through automatic code scanning and remediation directives for new code. This approach aims to prevent vulnerabilities from being introduced and addresses existing security debt through conversational remediation, significantly accelerating backlog clearance.

Library for continuous, automated threat modeling of AI-native applications, including LLMs and autonomous agents. Evo by Snyk directly connects to code, cloud infrastructure, and runtime data to generate dynamic system models that update with software evolution, eliminating drift. It provides contextual, actionable mitigations for vulnerabilities such as prompt injection, data exfiltration via AI models, and data poisoning, integrating into CI/CD pipelines for a secure-by-design workflow.

Library integrating Snyk and Continue automates security scans for code, dependencies, IaC, and containers using natural language commands within the developer workflow. This partnership enables faster vulnerability remediation through AI-generated, validated code fixes and proactive policy enforcement, allowing developers to address security without context switching. The integration supports Snyk's SAST, SCA, and IaC security tools directly in IDEs and CLIs, aiming to make "secure by default" a reality.

Library integrating Snyk's security intelligence with Qodo's Agentic Code Quality Platform. Snyk Studio for Qodo embeds security directly into the AI development workflow, leveraging Snyk's SAST and SCA engines. This allows developers to identify and fix vulnerabilities as they code within their IDE. The solution also addresses existing security debt through natural language prompts and automated remediation, aiming to resolve issues in minutes and accelerate secure AI-driven development at scale.

Library for scaling AI security, Evo by Snyk, complements agentic tools like OpenAI's Aardvark by offering stable, reproducible findings and integrating security earlier in the development lifecycle. It provides multi-layer AI threat detection, mature dynamic testing (DAST) and software composition analysis (SCA) engines, and native governance features to support enterprise workflows and compliance without unpredictable token-based costs.

Writeup of the SHA1-Hulud npm supply chain incident, a worm that infiltrates npm packages with hidden preinstall scripts. This advanced second wave of the Shai-Hulud attack targets developers and CI systems, deploying payloads that turn compromised machines into attacker-controlled GitHub Actions runners. It silently injects malicious workflows, exfiltrates GitHub and npm secrets, and harvests cloud credentials from AWS, Azure, and GCP, enabling remote command execution and broad system compromise. Some variants include destructive wiper capabilities.

Tool for AI-powered analysis of Snyk audit logs, transforming raw data into actionable intelligence for security and engineering leaders. Log Sniffer leverages Google Gemini AI to provide executive summaries, answer security questions in natural language, and monitor audit events in real-time. It seamlessly integrates with the Snyk API, offering intelligent filtering and transforming complex security events into understandable insights, improving decision-making and risk mitigation.

Library integration embedding Snyk Studio into Amazon Kiro’s agentic IDE, allowing developers to prevent new security risks at inception. This integration runs `snyk_code_scan` for generated code, attempts fixes with context from Snyk scans, and rescans to ensure resolution. It also addresses existing vulnerabilities through natural language prompts, identifying issues across code, dependencies, and IaC, then validating AI-generated fixes.

Tool for automating Model Context Protocol (MCP) server setup in AI-driven development environments. AutoMCP, an npm command-line tool, detects coding tools and project dependencies to configure MCP servers, enabling AI agents to autonomously run Snyk scans for early vulnerability detection. This integration, facilitated by Snyk Studio, embeds security directly into AI-assisted workflows, ensuring both human-written and AI-generated code is secure.

Library for securing AI-driven scientific missions, Snyk provides federal agencies with visibility into open source libraries, containers, and IaC templates within their software supply chains. It integrates security into CI/CD, model-training, and data pipelines, catching vulnerabilities and misconfigurations before deployment. The platform also addresses cloud and container security for AI compute systems, detecting misconfigurations and securing container images. By embedding security directly into developer workflows with automated fix recommendations and IDE plug-ins, Snyk operationalizes "secure by design" principles to accelerate discovery without compromising trust, aligning with federal expectations like Secure by Design, NIST 800-218, and EO 14028.

Library for agentic AI security orchestration, Evo by Snyk, addresses emergent threats like prompt injection, data poisoning, and supply chain risks inherent in AI-native applications. It automates security workflows, including AI Bill of Materials (AI-BOM) generation, MCP Scan CLI for identifying risky components, and continuous AI red teaming to keep pace with evolving AI systems, contrasting with traditional, manual application security methods.

Partnership between Snyk and Augment Code that embeds Snyk's security intelligence into Augment Code's AI development platform. This integration provides real-time security scanning as developers write code, accelerated agent-led remediation for identified vulnerabilities, and governance at scale through custom Snyk rules applied to AI-generated code. The solution aims to make "Secure by Default" a reality for AI-driven development, reducing mean time to remediate and eliminating security as a manual bottleneck.

Library for securing applications in the age of AI, the Snyk AI Security Platform operationalizes a prescriptive path. It addresses AI-accelerated DevSecOps by fortifying traditional software supply chains, secures AI-driven development by embedding security into coding assistants like Snyk Studio, and defends AI-native applications with the agentic security orchestrator Evo by Snyk. This unified approach weaves security directly into every stage of modern software creation, adapting to dynamic systems and operating at machine speed to build trust and mitigate risks introduced by AI.

Library for securing AI agents, focusing on the "google-qx4" malicious Google Skill on ClawHub that tricked users into installing malware via social engineering in the SKILL.md file. This technique bypasses traditional AppSec by leveraging agent-driven social engineering and legitimate-looking hosts like Rentry and GitHub, confirming "ToxicSkills" research predictions. It offers solutions like `mcp-scan` for skill analysis and Snyk AI-BOM for inventory, with Evo by Snyk providing AI-native security to monitor agent behavior and prevent malicious command execution.

Library enhancing Snyk Studio provides capabilities for securing AI-driven development, integrating with tools like Gemini CLI and Claude Code. It offers streamlined setup, real-time security guardrails, and introduces Remediation Directives for automated pull requests to fix vulnerabilities. New governance and control features, including an Adoption report, allow enterprises to manage and scale AI development securely, creating an AI Security Fabric.

Library for securing agent skill ecosystems, this resource details Snyk's integration with Vercel's skills.sh marketplace to perform automated security analysis on AI agent skills. It employs a deep multi-layer approach using LLM-based judges and deterministic rules to detect vulnerabilities in both code and natural language instructions, identifying "toxic flows" and prompt injection. The system aims for high recall on malicious skills with zero false positives, providing a "Security Verified" badge on skill pages and enabling continuous monitoring of the evolving threat landscape.

Library that unifies LLM-native capabilities with deterministic validation and operational automation to address the evolving application security landscape. It combines AI reasoning for discovery with robust enforcement mechanisms, addressing vulnerabilities introduced by AI-assisted development, including injection risks and business logic flaws. The library facilitates AI-accelerated DevSecOps, secures AI-driven development workflows through automated remediation directives, and extends protection to AI-native applications with visibility and policy enforcement, aiming to close the detection-to-remediation loop reliably.

Library that pairs uv, a high-performance Python package manager, with Snyk for application security. This integration enables native CycloneDX SBOM export from uv, allowing Snyk to scan dependencies for vulnerabilities and license compliance. The partnership also introduces native uv support within the Snyk CLI and IDE integrations, aiming to provide built-in security for AI-native Python applications, ensuring speed and security are not mutually exclusive.

Library for scanning agent skills in the Tessl Registry, integrating Snyk's security analysis to detect prompt injection, malware, and toxic flow patterns. This partnership provides real-time security scores on skill pages and search results, addressing the unique risks of agent skills by analyzing natural language instructions alongside code. The system automatically scans new skills and backfills existing ones, offering developers visibility into potential vulnerabilities before installation, inspired by Snyk's research into malicious skills and Snyk Learn lessons on agent goal hijack.

Library for securing generative AI, Evo AI-SPM, addresses AI sprawl and shadow AI through its Discovery Agent, which uncovers models and agents. It features Custom Discovery to detect bespoke AI implementations invisible to standard tools, and Snyk Generated Policies offering out-of-the-box, continuously enforced policies for governance. The Risk Intelligence Agent provides actionable risk signals for AI models, agents, and MCP servers, while the Policy Agent enables CI/CD pipeline enforcement and operational security for AI components.

Library enhancements from Snyk Container streamline inventory management with automated registry monitoring and customizable import/pruning rules. New beta features offer a unified platform experience, prioritize vulnerabilities based on runtime intelligence from third-party signals, and provide flexible support for multiple profiles in complex environments. These updates bolster security for the AI era by providing scalable visibility and automated remediation at the speed of agentic AI.

Library for detecting hardcoded secrets, designed to prevent "secret sprawl" and mitigate risks associated with AI-generated code and autonomous agents. It utilizes AI-powered, high-precision detection combining semantic and contextual analysis with custom regex. The library shifts left by integrating with IDEs and CLIs for real-time prevention, scans PR checks for merge-time detection, and offers unified reporting and ignore approval workflows within Snyk's broader AI Security Platform, addressing vulnerabilities exemplified by the ServiceNow Bodysnatcher incident.

Library for Snyk Agent Fix utilizing an agentic architecture, moving from static fine-tuning to dynamic few-shot prompting. This approach integrates Snyk's security intelligence, including a database of over 35,000 vulnerabilities and expert-written fixes, with frontier models like Anthropic's. Benchmarking focuses on security integrity (Pass@1/Pass@5), functional logic, and golden tests. The system supports agentic retries to adapt responses based on initial failures and offers full language coverage for all Snyk Code-supported languages, enabling faster, more secure code remediation.

Integration between Snyk and Atlassian offers intelligent, autonomous remediation for Jira security tickets. This solution leverages Snyk Studio's agentic skills, such as "snyk-fix" and "secure-at-inception," to autonomously generate and validate fixes within an Agentic Development Environment (ADE). By ingesting vulnerability data from Jira and utilizing Atlassian's TWG CLI or other CLIs, developers can reduce Mean Time to Resolution (MTTR), eliminate context switching, and improve fix accuracy, transforming security from a manual chore into an automated process.

Library compromise affecting Microsoft's `durabletask` Python package on PyPI, identified as SNYK-PYTHON-DURABLETASK-16761538, features a malicious payload including an infostealer targeting Linux systems, a worm for propagation, and a disk wiper. This incident is linked to the broader AntV Shai Hulud supply chain campaign, with threat actors expanding their targeting to include packages associated with major technology companies. Developers should verify their `durabletask` version and scan projects with Snyk.

Reference on Snyk's evolving go-to-market strategy, detailing its expansion beyond product-led growth to address the challenges of securing AI-generated code at scale. It highlights deep integrations with partners like Anthropic, Cursor, AWS, Atlassian, and OpenAI, and introduces a Partner Services Delivery Program and Partner Accelerator Fund designed to enable partners to build AI security practices and generate professional services revenue, emphasizing an ecosystem approach to application security in the AI era.

Library integrating Evo by Snyk with Anthropic's Claude Enterprise, providing security and compliance teams with an inventory of Claude environment models, MCP servers, risk signals, and tool-level permissions. Additionally, the Snyk Security Desktop Extension is now available for Claude Desktop on macOS and Windows, embedding real-time scanning and vulnerability context directly into developer workflows to catch issues at inception and ensure least privilege on AI agent tools.

Library for continuous offensive security testing, this resource details the evolution from traditional Dynamic Security Testing (DAST) to AI-driven pentesting. It distinguishes between heuristic-detectable vulnerabilities (like SQL injection) and context-dependent ones (such as BOLA and IDOR) that require human-like reasoning. The library enables automated discovery of context-dependent flaws by analyzing intent, and includes Agent Red Teaming for novel AI-specific attack surfaces like prompt injection and data exfiltration in LLM-integrated applications.

Library integrating Snyk with GitHub Copilot enables secure AI-assisted coding by shifting security left. Custom pre-commit hooks scan code in real-time, catching vulnerabilities like insecure dependencies during development. This empowers developers to fix issues immediately, reducing the mean time to remediate (MTTR) and accelerating technical growth.

Library for automating software composition analysis (SCA) remediation within the terminal. This tool empowers developers to address vulnerabilities at scale by integrating Snyk's security intelligence with large language models (LLMs). It analyzes findings, provides fix context including version upgrades and breakability analysis, and enables iterative, LLM-guided remediation loops with developer review, aiming to improve fix rates for SCA issues.

Library for generating and managing Software Bills of Materials (SBOMs), providing formal records of software components and their supply chain relationships. SBOMs enhance transparency, aid in vulnerability management, and support regulatory compliance, especially for software sold to the federal government as mandated by Executive Order 14028. Standards like SPDX, SWID, and OWASP CycloneDX are supported, enabling detailed analysis of dependencies, licenses, and potential exploits, complementing efforts like SLSA for supply chain integrity.

Library for detecting and preventing leaked secrets, including API keys, database passwords, cloud IAM credentials, and AI service keys. It addresses accidental commits to Git, insecure .env file practices, supply chain attacks via malicious packages like Shai-Hulud and compromised versions of TruffleHog, and leaks through non-code surfaces such as Slack, Jira, and Docker Hub. The library also highlights the growing risk from AI-assisted development and MCP server credentials, differentiating its secret scanning capabilities from SAST tools by emphasizing the analysis of full Git history, including deleted files.

Cheatsheet detailing eight Go security best practices for developers, emphasizing the use of Go Modules for dependency management and scanning dependencies for CVEs with tools like Snyk. It covers employing Go's standard crypto packages, utilizing `html/template` to prevent XSS attacks, exercising caution with subshelling, `unsafe`, and `cgo`, using reflection sparingly, and minimizing container attack surfaces.

Tutorial on Server-Side Request Forgery (SSRF) vulnerabilities, detailing how attackers can make arbitrary outbound requests from a server to access internal resources or cloud metadata. It demonstrates exploiting SSRF in a social app by reaching localhost and the AWS metadata endpoint (169.254.169.254), and discusses pivoting into internal networks, referencing CVE-2021-26084. The tutorial also covers prevention techniques like allowlisting and reconsidering dynamic request needs, noting SSRF's inclusion in the OWASP Top 10.

The content discusses a new CVE record, CVE-2025-8020, indicating that all Snyk versions of the package private-ip are vulnerable to Server-Side Request Forgery (SSRF). This vulnerability allows an attacker to input an IP or hostname that resolves to a multicast IP address (224.0.0.0/4), not included in the private IP range. This poses a security risk.

Tutorial on DOM XSS vulnerabilities, explaining how attackers manipulate the Document Object Model with client-side code injected via user-controllable sources like `eval()`, `document.write()`, or `innerHTML` sinks. It demonstrates exploiting a personalized profile color feature by escaping URL query parameters and recommends mitigating this by directly assigning color values to `document.body.style.color`, sanitizing input with libraries like `node-esapi`, or employing Content Security Policy (CSP) with nonces.

Cheatsheet detailing eight Go security best practices for Go developers. It covers using Go Modules for dependency management and scanning dependencies for CVEs with tools like Snyk. The resource recommends using Go's standard crypto packages and `html/template` to prevent XSS attacks. It also advises caution with subshelling, the `unsafe` package, and `cgo`, while recommending sparing use of reflection. Finally, it touches on minimizing container attack surfaces.