# Blog RSS Feed | Snyk Developer Security 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. ## 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. ## The Agent Baseline: 35 Controls, But Where Should You Start? DevFeed: [The Agent Baseline: 35 Controls, But Where Should You Start?]() Original publisher: [Read original article]() Author: Krysztof Huszcza; Ezra Tanzer Published: 2026-08-12T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Securing AI](), [Architecture & Design](), [Artificial Intelligence]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [application-security](), [architecture](), [black-hat](), [blog](), [coding](), [devops](), [docker](), [enablement](), [production](), [security]() ### AI overview The Agent Baseline presents six security outcomes, 35 controls, and an open reference architecture for running AI agents at enterprise scale. It addresses how organizations can decide where to begin, identify gaps, and sequence controls across coding, internal, and production agent use cases. ### Source excerpt The Agent Baseline defines 35 controls across six security outcomes--but the right starting point depends on how your organization uses agents. Learn how to sequence controls for coding, internal, and production agents. ## 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. ## 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. ## 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. ## 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. ## Stadium Summer: The Snyk Connect Fan Zone Tour DevFeed: [Stadium Summer: The Snyk Connect Fan Zone Tour]() Original publisher: [Read original article]() Author: Samantha Lioanag; Alysa Turner Published: 2026-07-29T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding]() Tags: [ai](), [ai-security](), [blog](), [community](), [developer](), [engineering](), [event](), [interest](), [security](), [skills](), [vibe-coding](), [workshops]() ### AI overview Snyk's Fan Zone tour combined AI security workshops, networking, and hands-on challenges across eight cities and three virtual sessions. Activities included chatbot hacks, vibe-coding contests, and a browser-focused DAST threat-modeling exercise. ### Source excerpt Snyk's Fan Zone tour brought AI security workshops, networking, and friendly competition to 8 cities and 3 virtual sessions. Attendees built skills, shared ideas, and leveled up together. ## 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. ## A Note to Our Customers and Partners DevFeed: [A Note to Our Customers and Partners]() Original publisher: [Read original article]() Author: Ken MacAskill Published: 2026-06-24T13:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Securing AI](), [Security for AI](), [Security](), [Vulnerabilities](), [agent observability]() Tags: [ai](), [ai-agents](), [ai-security](), [anthropic](), [blog](), [cursor](), [openai](), [security](), [software](), [vulnerabilities]() ### AI overview Snyk outlines an AI-focused organizational shift, continued investment in existing products, and workforce reductions. It describes its AI Security Platform for securing code, models, and intelligent systems, including visibility for AI assets, protection for AI agents, and planned offensive security for AI-era applications. ### Source excerpt A note to our customers and partners about Snyk's AI transformation and organizational changes. ## 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. [Next page]()