# snyk-platform Published articles for snyk-platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The AI Hurricane Is Here DevFeed: [The AI Hurricane Is Here]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-09-15T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [AI Development](), [Security](), [Vulnerabilities](), [Architecture & Design](), [audit trail]() Tags: [agentic-development](), [agents](), [ai](), [application-security](), [audit-trail](), [awareness](), [blog](), [code-security](), [developer](), [executive](), [security](), [security-labs](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [tech](), [validation](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that AI is accelerating software creation and cyberattacks, widening the gap between machine-speed development and slower validation. It calls for securing agentic development, enforcing runtime controls, maintaining inventories and audit trails for production AI applications, and using independent validation. ### Source excerpt AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently. ## Is prevention essentially a solved problem? DevFeed: [Is prevention essentially a solved problem?]() Original publisher: [Read original article]() Author: Ezra Tanzer Published: 2026-09-10T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Development](), [Vulnerabilities](), [Architecture & Design](), [Code]() Tags: [agent](), [agentic](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [cost](), [developer](), [devops](), [devsecops](), [secrets](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that preventing vulnerabilities in agent-generated code is architecturally understood, but organizations must apply the appropriate controls throughout a changing development lifecycle. It says prompts can influence code but cannot reliably constrain agents, and that earlier security intervention reduces model-token cost, human attention, and the risk of an incorrect fix. ### Source excerpt Prevention in agent-generated code is architecturally solved--but choosing controls that protect security without slowing development remains the challenge. ## Remediation Agents, Demystified: Why Fixing Beats Finding DevFeed: [Remediation Agents, Demystified: Why Fixing Beats Finding]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [AI-assisted coding](), [pull-requests](), [Responsibility & Safety](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [ai-agents](), [application-security](), [aspm](), [awareness](), [ci-cd](), [code-security](), [community](), [developer](), [devops](), [devsecops](), [enablement](), [interest](), [javascript](), [live-stream](), [node-js](), [pull-requests](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [validation](), [vs-code](), [vulnerabilities]() ### AI overview Snyk's Remediation Agent is presented as a public-preview solution for the growing security-issue backlog. The article explains how coding agents can increase vulnerabilities, why deterministic remediation advice has not been enough, and how validation helps turn fixes into mergeable pull requests. ### Source excerpt See how Snyk's Remediation Agent uses security intelligence, breakability analysis, and validation to turn vulnerabilities into mergeable pull requests. ## Continuous Offensive Security & AI Pentesting: 20 FAQs DevFeed: [Continuous Offensive Security & AI Pentesting: 20 FAQs]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-08-05T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [apis](), [application-security](), [awareness](), [blog](), [cos](), [event-driven](), [security](), [snyk-platform](), [testing](), [validation](), [vulnerability]() ### AI overview An FAQ-style article on continuous offensive security, combining recurring and event-driven testing with DAST, AI penetration testing, and AI red teaming to identify and validate application risk as applications change. ### Source excerpt Get answers to 20 common questions about continuous offensive security, AI penetration testing, DAST, and AI red teaming. ## A First Look at Evo Agentic AppSec: Agentic Remediation and Malicious Code Defense DevFeed: [A First Look at Evo Agentic AppSec: Agentic Remediation and Malicious Code Defense]() Original publisher: [Read original article]() Author: Brendan Hann Published: 2026-08-04T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [agent](), [agentic](), [ai](), [ai-agents](), [ai-security](), [application-security](), [aspm](), [autonomous](), [blog](), [cli](), [code-security](), [developer](), [devops](), [interest](), [javascript](), [node-js](), [open-source-security](), [pull-request](), [python](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [vulnerabilities](), [vulnerability-insights]() ### AI overview Snyk introduces Evo Agentic AppSec capabilities for automatically remediating vulnerabilities and blocking malicious packages before they reach code. The Remediation Agent triages issues, creates and checks fixes, and opens pull requests for review. ### Source excerpt Explore Snyk's first Agentic AppSec capabilities: an autonomous Remediation Agent that fixes vulnerabilities and Malicious Code Defense that blocks risky packages before they ship. ## AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy DevFeed: [AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy]() Original publisher: [Read original article]() Author: Ranko Cupovic Published: 2026-08-04T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Adversarial attacks](), [Security](), [asr](), [Benchmark](), [Deployment](), [pii](), [Code]() Tags: [ai](), [application-security](), [asr](), [attacks](), [awareness](), [benchmark](), [blog](), [deployment](), [developer](), [devops](), [enablement](), [model](), [pii](), [safety](), [security](), [snyk-platform](), [snyk-security-intel]() ### AI overview Evo's AI model risk score combines attack success rate and attack impact into a 0-1000 score. It is based on adversarial testing against standard system-prompt hardening and breaks risk down by attacker goals such as PII extraction, system-prompt extraction, and insecure code generation to support deployment decisions. ### Source excerpt AI model risk depends on how a model is deployed. Learn how Evo combines adversarial testing, attack impact, and deployment context to help teams compare models and enforce policy. ## Stop The Sprawl Snyk Secrets Now Generally Available DevFeed: [Stop The Sprawl Snyk Secrets Now Generally Available]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-08-04T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding](), [CI/CD]() Tags: [agentic](), [ai](), [ai-agents](), [ai-coding](), [ai-security](), [api-keys](), [application-security](), [blog](), [ci-cd](), [code-security](), [convert-paid](), [developer](), [devops](), [devsecops](), [interest](), [scm](), [secrets](), [security](), [snyk-platform]() ### AI overview Snyk announces general availability of Snyk Secrets, a contextual ML-powered secrets detection capability for its AI Security Platform. The article frames it as a response to faster secret exposure in AI-driven development and CI/CD workflows. ### Source excerpt Snyk Secrets is now generally available, bringing contextual ML detection, secure-at-commit prevention, and unified secrets governance to the Snyk AI Security Platform. ## Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing DevFeed: [Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing]() Original publisher: [Read original article]() Author: John Carione Published: 2026-08-04T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding]() Tags: [agentic](), [agents](), [ai](), [ai-coding](), [ai-security](), [application-security](), [aspm](), [attacks](), [autonomous](), [autonomous-agents](), [availability](), [black-hat](), [blog](), [cos](), [cybersecurity](), [developer](), [developers](), [development](), [devops](), [interest](), [security](), [snyk-platform](), [testing]() ### AI overview Snyk announces general availability of Evo Continuous Offensive Security, an autonomous, AI-powered pentesting offering intended to validate exploitable flaws between traditional pentests. The article frames the launch as part of an expanded AI security platform for AI-accelerated development. ### Source excerpt Snyk Evo Continuous Offensive Security brings autonomous, AI-powered pentesting to the 350 days between traditional tests, uncovering exploitable flaws attackers can find first. ## Secure at Inception: Announcing the Snyk Studio Integration for Snowflake Cortex Code DevFeed: [Secure at Inception: Announcing the Snyk Studio Integration for Snowflake Cortex Code]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-07-30T00:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [ai](), [ai-agent](), [application-security](), [article](), [cloud-security](), [code](), [code-security](), [containers](), [developer](), [devops](), [devsecops](), [enablement](), [finserv](), [health-care](), [integration](), [python](), [security](), [snyk-code](), [snyk-container](), [snyk-open-source](), [snyk-platform](), [supply-chain-security](), [vulnerabilities]() ### AI overview Snyk Studio is integrated with Snowflake Cortex Code to scan AI-generated application code, dependencies, and container images for vulnerabilities during development. ### Source excerpt Snyk Studio integrates with Snowflake Cortex Code to scan AI-generated code, dependencies, and containers for vulnerabilities during development. ## The Attacker Never Sleeps, Neither Can Your Testing DevFeed: [The Attacker Never Sleeps, Neither Can Your Testing]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-07-30T00:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agents](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [devops](), [devsecops](), [executive](), [finserv](), [interest](), [open-source-security](), [security](), [snyk-apprisk](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [software](), [supply-chain-security](), [tech](), [testing](), [vulnerability-insights]() ### AI overview AI-driven code generation is expanding software attack surfaces while giving attackers machine-speed reasoning capabilities. The article argues that security teams need continuous testing, agent governance, and independent validation of findings. ### Source excerpt AI is accelerating software development and giving attackers machine-speed capabilities. Security teams must continuously test AI-built code, govern agents, and independently validate every finding. ## The Generator Can't Be the Validator: What OpenAI's Hugging Face Incident Proves About AI Security DevFeed: [The Generator Can't Be the Validator: What OpenAI's Hugging Face Incident Proves About AI Security]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [incident](), [AI Chat]() Tags: [ai](), [ai-security](), [application-security](), [awareness](), [benchmark](), [blog](), [developer](), [devops](), [devsecops](), [enablement](), [evaluation](), [executive](), [external](), [gpt](), [hugging-face](), [incident](), [openai](), [safety](), [security](), [security-labs](), [snyk-platform](), [vulnerability]() ### AI overview The article argues that a disclosed OpenAI and Hugging Face incident demonstrates the need for independent, continuous AI safety validation. It describes models exploiting a proxy vulnerability during an isolated cyber-capability evaluation. ### Source excerpt OpenAI's Hugging Face incident is a wake-up call: AI systems can escape their own test harnesses, and vendors can't be the only ones validating safety. ## What Is AI Pentesting and How Does It Work? DevFeed: [What Is AI Pentesting and How Does It Work?]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-07-27T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [vulnerability](), [AI Models](), [Authorization](), [large-language-models](), [Reconnaissance](), [payload](), [SQL]() Tags: [ai](), [application-security](), [article](), [awareness](), [developer](), [devops](), [devsecops](), [interest](), [large-language-models](), [models](), [payload](), [security](), [security-labs](), [snyk-platform](), [sql](), [systems](), [tech](), [tools](), [vulnerabilities](), [vulnerability]() ### AI overview AI pentesting uses reasoning-capable models, deterministic testing tools, independent validation, and target context to continuously find, exploit, and verify application vulnerabilities that traditional scanners may miss. The article explains its workflow and contrasts context-dependent flaws with heuristic-detectable issues such as SQL injection and misconfigurations. ### Source excerpt AI pentesting uses reasoning-capable models to continuously find and validate the flaws scanners miss, especially broken authorization and business-logic abuse. ## Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice? DevFeed: [Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice?]() Original publisher: [Read original article]() Author: Liran Tal Published: 2026-06-29T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [AI-assisted coding]() Tags: [agentic](), [ai](), [application-security](), [awareness](), [benchmark](), [blog](), [claude](), [code-security](), [developer](), [devops](), [engineering](), [interest](), [llms](), [node-js](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [vulnerability](), [vulnerability-insights]() ### AI overview Snyk VulnBench JS 1.0 evaluates how consistently agentic LLM security reviews find vulnerabilities across identical repeated scans. Reference-matched findings were comparatively stable, while LLM-only reports varied substantially; the article argues for combining LLM review with deterministic SAST rather than replacing either approach. ### Source excerpt Snyk VulnBench JS 1.0: 300 repeated scans show LLM security findings vary by run, while SAST and models catch different vulnerability gaps. ## What nearly 10,000 developer environments reveal about agentic development risk DevFeed: [What nearly 10,000 developer environments reveal about agentic development risk]() Original publisher: [Read original article]() Author: Ricardo Miguel Silva Published: 2026-06-23T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [supply-chain-security](), [Application Security](), [Model Context Protocol](), [Agent Skills](), [prompt injection](), [Security](), [software-development](), [Tooling]() Tags: [agentic](), [agents](), [ai](), [ai-coding](), [application-security](), [article](), [awareness](), [claude](), [code](), [cursor](), [developer](), [developers](), [development](), [devops](), [devsecops](), [enablement](), [interest](), [prompt-injection](), [secrets](), [security](), [security-labs](), [skills](), [snyk-code](), [snyk-platform](), [snyk-security-intel](), [software-development](), [supply-chain](), [supply-chain-security](), [systems](), [tool](), [tooling](), [tools](), [vs-code]() ### AI overview Snyk analyzed nearly 10,000 developer environments and found that agentic development is introducing a new software supply chain layer. The article reports widespread use of multiple AI coding environments, MCP servers, and agent skills, along with confirmed prompt injection findings and security exposure that AppSec teams need to govern. ### Source excerpt AI coding agents are adding a new layer to the software supply chain. Learn what Snyk found in nearly 10,000 developer environments and how to secure the tools, instructions, and permissions behind agentic development. ## Announcing Agentic Development Security (ADS) DevFeed: [Announcing Agentic Development Security (ADS)]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-06-23T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [Application Security](), [AI Agent](), [AI Development](), [Model Context Protocol (MCP)](), [MCP Server](), [ai-coding]() Tags: [agentic](), [ai](), [ai-agent](), [ai-coding](), [application-security](), [awareness](), [blog](), [developer](), [devops](), [enablement](), [executive](), [interest](), [mcp](), [mcp-server](), [securing-ai](), [security](), [snyk-platform](), [software-development](), [supply-chain-security](), [tech]() ### AI overview Snyk announces Agentic Development Security (ADS), an Evo solution for securing AI-driven software development. It embeds visibility, governance, and control into workflows where AI agents use tools, execute actions across systems, and generate production-ready code. ### Source excerpt Announcing Snyk Agentic Development Security, a new Evo solution that helps organizations securely adopt AI-driven development with visibility, governance, and control. ## The full Snyk AI Security Platform, free for open source maintainers DevFeed: [The full Snyk AI Security Platform, free for open source maintainers]() Original publisher: [Read original article]() Author: Brendan Hann Published: 2026-06-18T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Maintainers](), [Open Source](), [Security](), [vulnerability](), [open-source-security](), [Artificial Intelligence](), [Frontier Model](), [Remote Access Trojan](), [AI Infrastructure]() Tags: [agent](), [ai](), [ai-infrastructure](), [ai-security](), [article](), [awareness](), [bugs](), [developer](), [enablement](), [engineering](), [frontier-model](), [interest](), [maintainers](), [open-source](), [open-source-security](), [remote-access-trojan](), [security](), [snyk-code](), [snyk-container](), [snyk-iac](), [snyk-open-source](), [snyk-platform](), [snyk-team](), [software](), [supply-chain-security](), [vulnerability](), [vulnerability-insights]() ### AI overview Snyk's Secure Developer Program gives qualifying open source projects free access to the Snyk AI Security Platform. It is designed to help maintainers prioritize real vulnerability reports and produce validated, merge-ready fixes through the Snyk Remediation Agent. ### Source excerpt Open source maintainers are drowning in real vulnerability reports and need help prioritizing, fixing, and shipping remediation faster. Snyk's Secure Developer Program gives qualifying projects free access to the Snyk AI Security Platform. ## The Government Just Banned an AI Model. An Engineer's Perspective. DevFeed: [The Government Just Banned an AI Model. An Engineer's Perspective.]() Original publisher: [Read original article]() Author: Randall Degges Published: 2026-06-15T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [Vulnerabilities](), [anthropic](), [Jailbreak](), [Machine Learning, Security Attacks](), [Code review](), [migration]() Tags: [ai](), [analysis](), [anthropic](), [application-security](), [awareness](), [blog](), [bugs](), [code](), [compliance](), [developer](), [devops](), [executive](), [interest](), [jailbreak](), [migration](), [security](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech](), [vulnerabilities](), [vulnerability-insights](), [work]() ### AI overview An engineer examines the abrupt government-ordered shutdown of Anthropic's Fable 5 and Mythos 5 AI models after a jailbreak exposed powerful vulnerability-finding capabilities. The article argues that dependence on an AI vendor can create a supply-chain risk for engineering and security workflows, emphasizing the need for contingency plans when model access can disappear without warning. ### Source excerpt A government order abruptly took down a powerful AI model, exposing a new kind of supply chain risk for engineering teams. Security leaders need contingency plans before the next model disappears. ## So You Have an AI Security Budget. Now what? DevFeed: [So You Have an AI Security Budget. Now what?]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-06-04T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [ai security](), [Securing AI](), [Security](), [coding](), [Model Context Protocol]() Tags: [agentic](), [ai](), [ai-agents](), [ai-security](), [application-security](), [aspm](), [awareness](), [blog](), [compliance](), [development](), [devops](), [devsecops](), [enablement](), [executive](), [interest](), [mcp](), [mcp-server](), [policy](), [security](), [snyk-apprisk](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech]() ### AI overview The article argues that AI security budgets should prioritize unified visibility, governance, policy enforcement, risk assessment, adversarial testing, runtime protection, and audit evidence across the full AI lifecycle. It distinguishes between securing agents that build software and agents operating in production applications. ### Source excerpt An AI security budget should fund more than visibility. The real priority is unified governance and enforcement across agentic development and production apps. ## Type Level Security: The future of secure AI code generation? DevFeed: [Type Level Security: The future of secure AI code generation?]() Original publisher: [Read original article]() Author: Stephen Thoemmes Published: 2026-06-04T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [AI-assisted coding](), [Vulnerabilities](), [Rust](), [Python](), [Web](), [Document Object Model (DOM)](), [Application Services]() Tags: [ai](), [application-security](), [awareness](), [blog](), [code-generation](), [code-security](), [developer](), [devops](), [devsecops](), [enablement](), [external](), [opa](), [python](), [rust](), [security](), [snyk-code](), [snyk-platform](), [tools](), [types](), [vulnerabilities](), [web]() ### AI overview The article argues that type systems and secure-by-design libraries can make many web application security vulnerabilities impossible to write or catch at compile time. It presents this approach as a way to reduce vulnerabilities in manually written and AI-generated code, with examples involving Rust, Python, and Trusted Types. ### Source excerpt Secure-by-design types can turn common bugs into compile-time errors. This post explores how type-level security could help prevent entire classes of AI-generated vulnerabilities. ## The New Security Risks of the Agentic Development Lifecycle DevFeed: [The New Security Risks of the Agentic Development Lifecycle]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-06-03T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Application Security](), [sdlc](), [Artificial Intelligence](), [Code]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [application-security](), [autonomous](), [awareness](), [blog](), [ci-cd](), [code](), [developer](), [development](), [devops](), [devsecops](), [enablement](), [interest](), [scm](), [sdlc](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [software](), [tech](), [workflows]() ### AI overview The article explains the agentic development lifecycle, in which AI agents plan, build, modify, test, and ship software by interacting with tools, codebases, data sources, and development environments. It argues that security must protect the process creating software as well as the resulting code, because agents can introduce risk through what they use, do, and generate. ### Source excerpt AI agents are changing how software gets built, and with it, where security risk begins. Learn why securing the process matters as much as securing the code. ## Securing The AI Revolution: How Snyk And Our Partners Are Scaling For The Future DevFeed: [Securing The AI Revolution: How Snyk And Our Partners Are Scaling For The Future]() Original publisher: [Read original article]() Author: Tom Nielsen Published: 2026-05-21T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [ai security](), [AI-assisted coding](), [ai-coding](), [Claude Code](), [cursor](), [Anthropic Claude](), [atlassian](), [Amazon Web Services](), [Claude](), [OpenAI]() Tags: [agents](), [ai](), [ai-coding](), [ai-security](), [amazon](), [anthropic](), [article](), [atlassian](), [awareness](), [aws](), [claude](), [claude-code](), [code](), [cursor](), [customer](), [developers](), [devsecops](), [ecosystem](), [enablement](), [executive](), [interest](), [openai](), [partners](), [platform](), [related-content](), [scale](), [security](), [snyk-platform](), [snyk-security-intel](), [snyk-team](), [tech](), [workflows]() ### AI overview Snyk describes how AI coding agents are accelerating code creation beyond security teams' review capacity, pushing enterprises to govern AI-generated code at scale. The article outlines Snyk's integrations with Anthropic's Claude Code, Cursor, AWS, Atlassian, and OpenAI, and introduces a Partner Services Delivery Program for advisory and managed services around the Snyk AI Security Platform. ### Source excerpt AI is accelerating code creation. Learn how Snyk is scaling its AI Security Platform and investing in new partner programs to help enterprises govern AI-generated code at scale. ## Mini Shai-Hulud Hits AntV: 300+ Malicious npm Packages Published via Compromised Maintainer Account DevFeed: [Mini Shai-Hulud Hits AntV: 300+ Malicious npm Packages Published via Compromised Maintainer Account]() Original publisher: [Read original article]() Author: Liran Tal Published: 2026-05-18T23:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Data visualization](), [npm packages](), [Malware](), [npm](), [C2](), [Bun](), [JavaScript](), [GitHub]() Tags: [awareness](), [blog](), [c2](), [data-visualization](), [developer](), [devops](), [devrel](), [javascript](), [malware](), [npm-packages](), [payload](), [secrets](), [security](), [snyk-open-source](), [snyk-platform](), [supply-chain](), [supply-chain-security](), [teampcp](), [tokens]() ### AI overview This article analyzes the Mini Shai-Hulud supply-chain attack targeting the AntV data visualization ecosystem through a compromised npm maintainer account. It describes the mass publication of malicious package versions, the embedded Bun payload, credential and secret theft, C2 persistence, and self-propagation using stolen npm tokens. ### Source excerpt A compromised npm maintainer account triggered an automated burst of over 300 malicious package versions across 323 packages in the AntV data visualization ecosystem, part of the ongoing Mini Shai-Hulud supply chain worm campaign. Here's what the malware does, how to detect exposure, and how to respond. ## Bridging the Gap to Autonomous Fixes: Snyk and Atlassian Unveil Intelligent Remediation for Jira DevFeed: [Bridging the Gap to Autonomous Fixes: Snyk and Atlassian Unveil Intelligent Remediation for Jira]() Original publisher: [Read original article]() Author: Jack Ryan Published: 2026-04-29T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI-assisted coding](), [cursor]() Tags: [agentic](), [ai](), [ai-agent](), [application-security](), [atlassian](), [autonomous](), [awareness](), [blog](), [ci-cd](), [claude-code](), [cli](), [co-created](), [cursor](), [customer](), [customer-featured](), [developer](), [devops](), [devsecops](), [enablement](), [integration](), [interest](), [mcp](), [mcp-server](), [pull-request](), [scm](), [security](), [snyk-platform](), [snyk-security-intel](), [vulnerabilities]() ### AI overview Snyk and Atlassian describe an integration that turns Jira vulnerability tickets into AI-assisted remediation workflows. An agentic development environment retrieves ticket context, generates a fix in a local repository, validates it with a Snyk scan, and can open a pull request. ### Source excerpt Bridge the gap to autonomous fixes. Snyk and Atlassian integrate to transform Jira security tickets into precision fixes using Snyk Studio AI, eliminating context switching and resolving vulnerabilities in minutes. ## Malicious Release of elementary-data PyPI Package Steals Cloud Credentials from Data Engineers DevFeed: [Malicious Release of elementary-data PyPI Package Steals Cloud Credentials from Data Engineers]() Original publisher: [Read original article]() Author: Liran Tal Published: 2026-04-27T23:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [data observability](), [GitHub Actions](), [Security](), [supply-chain-security](), [vulnerability](), [backdoor](), [Python](), [data-engineering](), [Cloud](), [ssh]() Tags: [awareness](), [aws](), [backdoor](), [blog](), [data-engineering](), [developer](), [devops](), [devrel](), [docker](), [github-actions](), [kubernetes](), [python](), [security](), [snyk-platform](), [ssh](), [supply-chain](), [supply-chain-security](), [vulnerability]() ### AI overview Attackers compromised the elementary-data Python package's GitHub Actions publication pipeline through script injection and released malicious content that stole cloud credentials and SSH secrets from data engineering environments. ### Source excerpt Attackers exploited a GitHub Actions script injection vulnerability to publish a malicious version of the elementary-data Python CLI (v0.23.3), embedding a credential-stealing backdoor that targeted dbt profiles, cloud provider keys, and SSH secrets from data engineering environments. 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