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Autonomous attackers are shrinking the window for defense. Learn how continuous discovery, remediation, validation, and prevention can help security teams keep pace.
TOKYO -- Japan's disclosure system for large shareholding positions faces scrutiny after Situational Awareness, a U.S. hedge fund that drew widespread
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.
A growing vulnerability backlog is more than technical debt: it is an attack surface. Learn why outdated risk assumptions, automated attackers, and chained findings demand a new approach.
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.
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.
See how Snyk’s Remediation Agent uses security intelligence, breakability analysis, and validation to turn vulnerabilities into mergeable pull requests.
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.
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.
Get answers to 20 common questions about continuous offensive security, AI penetration testing, DAST, and AI red teaming.
keyv 6.0.0 and ten related npm releases shipped install-time malware. See affected versions, hashes, detection steps, and safe remediation order.
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.
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.
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.
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.
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