14 stories
AI coding agents can produce authorization logic that compiles and passes review while exposing one tenant’s data to another. Learn why broken access control is difficult to detect and how to prevent it.
Learn how Agentic AppSec uses grounded, bounded, and independently verified AI agents to run the application security loop.
Evo ADS Govern Agent Behavior is now generally available, starting with MCP Governance. Discover, approve, monitor, log, and block MCP server usage across leading AI coding agents.
Ask your Evo tenant about models, MCP servers, and skills across your AI estate, from the client you already work in.
AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently.
Prevention in agent-generated code is architecturally solved—but choosing controls that protect security without slowing development remains the challenge.
Get answers to 20 common questions about continuous offensive security, AI penetration testing, DAST, and AI red teaming.
Snyk Evo Continuous Offensive Security brings autonomous, AI-powered pentesting to the 350 days between traditional tests, uncovering exploitable flaws attackers can find first.
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.
Snyk Secrets is now generally available, bringing contextual ML detection, secure-at-commit prevention, and unified secrets governance to the Snyk AI Security Platform.
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.
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.
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: 300 repeated scans show LLM security findings vary by run, while SAST and models catch different vulnerability gaps.
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