# awareness Published articles for awareness. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Thinking Like A Naturalist: An Interview with Claire Barrett DevFeed: [Thinking Like A Naturalist: An Interview with Claire Barrett]() Original publisher: [Read original article]() Author: J Simpson Published: 2026-09-15T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [Artificial Intelligence](), [systems](), [Testing]() Tags: [2026](), [ai](), [ai-agents](), [ai-readiness](), [api-governance](), [api-integration](), [api-security](), [artificial-intelligence](), [awareness](), [blog](), [digital-transformation](), [driving](), [generative-ai](), [governance](), [integration](), [summit](), [systems](), [testing]() ### AI overview Claire Barrett discusses the mindset shift needed for AI-enabled strategies, emphasizing systems thinking, governance, testing, awareness, and attention to long-term risks. ### Source excerpt Ahead of Nordic APIs Summit 2026, we catch up with speaker Claire Barrett on the mindset shift needed for adopting AI-enabled strategies. "Imagine driving around an old town," responds Claire Barrett when asked to describe what it means to be AI-ready from an integration perspective. "Imagine a European city that's been inhabited for thousands of ... ## 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. ## Why Your AI Application Is Exposed DevFeed: [Why Your AI Application Is Exposed]() Original publisher: [Read original article]() Author: Damian Tommasino Published: 2026-08-26T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [AI Development](), [Large Language Model](), [vulnerability](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [Machine Learning, Security Attacks](), [data]() Tags: [ai](), [ai-red-team](), [application-security](), [awareness](), [blog](), [devops](), [executive](), [interest](), [llm](), [mcp](), [rag](), [security](), [testing](), [vulnerability]() ### AI overview The article explains why AI applications can pass conventional security scans and model evaluations while remaining exploitable through chained interactions among prompts, language models, retrieval, tools, and backend workflows. It presents DAST, AI penetration testing, and AI red teaming as complementary lenses for identifying exposed surfaces, exploitable paths, and real-world adversary outcomes. ### Source excerpt AI applications can pass security scans yet remain exploitable through chained attacks across models, tools, data, and business workflows. Learn how DAST, AI pentesting, and red teaming work together to expose end-to-end risk. ## 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. ## Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14% DevFeed: [Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14%]() Original publisher: [Read original article]() Author: Stephen Thoemmes Published: 2026-08-18T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [benchmarking](), [LLM evaluation / benchmarking](), [vulnerability](), [AI-assisted coding](), [JavaScript](), [Python]() Tags: [agentic](), [ai](), [awareness](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [code-security](), [developer](), [devops](), [evaluation](), [interest](), [java](), [javascript](), [performance](), [python](), [security](), [security-labs](), [snyk-code](), [snyk-security-intel](), [vulnerability](), [vulnerability-insights]() ### AI overview A benchmark of about 150 vulnerable JavaScript, Java, and Python samples evaluates whether frontier models produce fixes that are both secure and functional. The article reports that models working alone reach roughly 72-75%, while Snyk Intelligence raises Opus 4.6 from 74.6% to 85.4% and improves Python results from 64% to 88%. ### Source excerpt A benchmark of secure, functional vulnerability fixes across JavaScript, Java, and Python shows Snyk Intelligence helps frontier models break past a 72-75% performance plateau. ## Coding With OpenCode: AI-Assisted Python DevFeed: [Coding With OpenCode: AI-Assisted Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-11T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [AI Chat]() Tags: [agent](), [ai](), [ai-coding](), [anthropic](), [api](), [awareness](), [code](), [coding](), [developer](), [gemini](), [google](), [how-to](), [learn](), [open-source](), [openai](), [programming](), [python](), [tutorial](), [video](), [work]() ### AI overview A video course on using OpenCode, an open-source AI coding agent, to analyze, refactor, and troubleshoot Python projects from the terminal. ### Source excerpt Learn how to use OpenCode for AI-assisted Python coding, using a free Gemini API key to analyze and refactor code right in your terminal. ## Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS DevFeed: [Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS]() Original publisher: [Read original article]() Author: Nuno Loureiro; Luis Grangeia Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Vulnerabilities](), [Authorization](), [Software as a service](), [Microservice](), [Microservices](), [Front end](), [API](), [App](), [Artificial Intelligence](), [Trustworthy AI]() Tags: [agent](), [ai](), [api](), [application-security](), [applications](), [attacks](), [autonomous](), [awareness](), [blog](), [customer](), [customer-featured](), [cybersecurity](), [devops](), [enterprise](), [frontend](), [interest](), [microservices](), [saas](), [security](), [vulnerabilities](), [vulnerability-insights](), [web]() ### AI overview Evo Continuous Offensive Security (COS) is presented as an autonomous offensive-security system combining AI pentesting, agent red teaming, and dynamic testing. The article reports a real assessment of a multi-tenant enterprise SaaS application that uncovered 33 confirmed vulnerabilities, including tenant-wide compromise and critical authorization flaws. ### Source excerpt A real Evo Continuous Offensive Security assessment uncovered 33 confirmed vulnerabilities in a multi-tenant enterprise SaaS, including tenant-wide compromise and critical authorization flaws. ## 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. ## 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. ## Inside the keyv npm Compromise: preinstall Malware, Trusted Provenance, and IDE Hooks DevFeed: [Inside the keyv npm Compromise: preinstall Malware, Trusted Provenance, and IDE Hooks]() Original publisher: [Read original article]() Author: Liran Tal; Lion Kontorer Published: 2026-08-04T04:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [incident](), [releases]() Tags: [awareness](), [blog](), [ci](), [ci-cd](), [code](), [developer](), [devops](), [embedded](), [enablement](), [ide](), [incident](), [javascript](), [malware](), [manifest](), [node-js](), [npm-packages](), [open-source-security](), [payload](), [persistence](), [releases](), [research](), [scm](), [secrets](), [security](), [security-labs](), [snyk-learn](), [snyk-open-source](), [snyk-security-intel](), [software](), [supply-chain](), [supply-chain-security](), [vulnerability-insights]() ### AI overview Snyk reports an active supply-chain incident in which keyv 6.0.0 and ten related npm releases contained install-time malware. The article describes the malicious preinstall hook, its second-stage payload, affected-release investigation, and remediation guidance. ### Source excerpt keyv 6.0.0 and ten related npm releases shipped install-time malware. See affected versions, hashes, detection steps, and safe remediation order. ## 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. ## Symlinks Are Still Scary (And Yes, You Can Commit Them to Git) DevFeed: [Symlinks Are Still Scary (And Yes, You Can Commit Them to Git)]() Original publisher: [Read original article]() Author: Randall Degges Published: 2026-07-09T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Git](), [ai-coding](), [AI Agent](), [Unix](), [GitHub]() Tags: [ai](), [ai-agent](), [ai-coding](), [application-security](), [article](), [awareness](), [code](), [developer](), [devops](), [devsecops](), [git](), [github](), [scm](), [secrets](), [security](), [security-labs](), [snyk-security-intel]() ### AI overview Symlink attacks exploit the way operating systems transparently redirect symbolic links to other paths. A malicious symlink committed to a Git repository can cause build scripts, editors, or AI coding tools to access unintended files, potentially enabling remote code execution. ### Source excerpt A harmless-looking symlink in a Git repo can redirect a tool into reading or writing anywhere on your machine. That old trick is now showing up in AI coding assistants, with nasty results. ## 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. ## NVD in the AI Era: The Case for Multi-Source Vulnerability Intelligence DevFeed: [NVD in the AI Era: The Case for Multi-Source Vulnerability Intelligence]() Original publisher: [Read original article]() Author: Michelle Ginzburg; Noa Yaffe-Ermoza Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [open-source-security](), [Security](), [data]() Tags: [ai](), [article](), [awareness](), [developer](), [devops](), [interest](), [open-source](), [open-source-security](), [security](), [snyk-open-source](), [snyk-security-intel](), [software](), [supply-chain-security](), [technology](), [vulnerability](), [vulnerability-insights]() ### AI overview The article explains why vulnerability intelligence is moving beyond reliance on the National Vulnerability Database. It describes NIST's shift toward prioritized triage and presents Snyk's multi-source approach, combining vulnerability advisories, analyst validation, internal enrichment, open-source context, and multiple risk assessments to help teams prioritize remediation. ### Source excerpt NIST's shift to risk-based enrichment makes one thing clear: modern security teams need more than a single public source. In the AI era, trusted vulnerability intelligence depends on multiple signals, human validation, and clear context. ## The New Security Control Point: Governing AI Agents Inside the Execution Loop DevFeed: [The New Security Control Point: Governing AI Agents Inside the Execution Loop]() Original publisher: [Read original article]() Author: Agnieszka Koc Published: 2026-06-23T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [Application Security](), [AI Development](), [agent observability](), [real-time]() Tags: [agents](), [ai](), [ai-agents](), [api](), [application-security](), [autonomous](), [awareness](), [blog](), [code-security](), [developer](), [interest](), [process](), [security](), [software-development]() ### AI overview AI agents introduce security risk through the actions they take while using tools, accessing systems, invoking APIs, executing commands, and generating code. The article presents agent behavior governance inside the execution loop as a security control point for evaluating whether decisions are authorized, appropriate, and aligned with organizational policy. ### Source excerpt AI agents introduce security risk through the actions they take, not just the code they produce. Learn how agent behavior governance helps teams observe, steer, and block risky actions in real time. ## 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. ## Sub-agents: splitting context across specialized AI agents DevFeed: [Sub-agents: splitting context across specialized AI agents]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Bots](), [Language models]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [awareness](), [complexity](), [cost](), [crm](), [memory](), [model](), [prompt-engineering](), [reasoning](), [redis](), [retrieval](), [routing](), [tech-de](), [tool](), [tools](), [workers]() ### AI overview The article explains how specialized AI sub-agents divide complex work, coordinate through patterns such as orchestrator-worker, supervisor, swarm, and router, and contend with context, stale state, and shared-memory challenges. ### Source excerpt If you've ever watched a single AI agent lose the plot on a complex task, you've probably wondered whether splitting the work across multiple agents would help. It can. But the split comes with its own headaches. Agents lose track of each other's work... ## 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. ## A Day in the Life of an AI Engineer in Snyk's Lisbon Office DevFeed: [A Day in the Life of an AI Engineer in Snyk's Lisbon Office]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-06-16T23:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Securing AI](), [Code]() Tags: [ai](), [ai-security](), [awareness](), [blog](), [code](), [collaboration](), [community](), [design](), [recruiting](), [security]() ### AI overview A first-person account of a typical day for an AI Engineer at Snyk's Lisbon office, covering commuting, focused technical work, virtual collaboration across time zones, and the balance of work and breaks. ### Source excerpt Explore a day in the life of an AI Engineer at Snyk's Lisbon office. See what it's like building AI-powered security tools, collaborating globally, and enjoying the vibrant culture of Portugal's capital city. ## A Forgotten Contributor Account Compromised the Entire Mastra npm Package Scope DevFeed: [A Forgotten Contributor Account Compromised the Entire Mastra npm Package Scope]() Original publisher: [Read original article]() Author: Liran Tal; Marian Corneci Published: 2026-06-16T21:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [incident]() Tags: [awareness](), [blog](), [ci-cd](), [cross-platform](), [cryptocurrency](), [developer](), [devops](), [devrel](), [devsecops](), [incident](), [javascript](), [node-js](), [open-source-security](), [payload](), [persistence](), [remote-access-trojan](), [sbom](), [scm](), [secrets](), [security](), [snyk-open-source](), [supply-chain](), [supply-chain-security](), [tech](), [tls](), [vulnerability-insights]() ### AI overview A dormant former-contributor npm account was compromised and used to republish the Mastra package scope with a malicious dependency that installs cryptocurrency-stealing malware and a persistent remote-access trojan. The article describes the stale access control that enabled the supply-chain incident and advises treating affected installations as credential and wallet exposure events. ### Source excerpt A dormant contributor account was used to republish the entire @mastra npm scope, each injected with a single dependency, easy-day-js, that drops a cross-platform cryptocurrency stealer. Here is how the attack worked, how to check exposure, and how to remediate. [Next page]()