Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For the past year, I've described this as an AI fog: competing claims, uncertain risks, and leaders struggling to see what mattered. The winds are up now. AI is accelerating software creation, exposing years of accumulated vulnerabilities, and handing both attackers and defenders capabilities that operate at machine speed.

Stopping new security issues in agent-generated code from being deployed is, architecturally, a solved problem. Prevention is the act of keeping a new vulnerability in code from reaching production at any point in the development and release process, including but not limited to preventing its introduction in a feature branch.

Imagine getting three separate security reports back for your new enterprise AI assistant: On paper, the application looks ready for production, but in reality, a threat actor bypasses your guardrails in minutes. How? By using the AI model as an intermediary. The attacker steers the LLM to invoke the internal utility tool, thereby bridging an untrusted prompt directly to the backend execution sink.

Six new security issues for every one issue remediated. That's the ratio Snyk research has found, and it's why the AI Security Engineers Community gave an hour of livestream time to fixing rather than finding. Play Video: Remediation Agents Demystified: Your AI Teammate for Fixing Security Bugs Remediation Agents Demystified paired a fireside chat with a live demo.

We benchmarked how well leading models produce vulnerability fixes that are both secure and functional, across ~150 real vulnerable code samples in JavaScript, Java, and Python. We ran each model on its own and with Snyk Intelligence (the new agentic Agent Fix architecture). The headline findings.

Two weeks ago, we published the Agent Baseline alongside Docker and Keycard. In it, we describe six security outcomes, 35 controls, and an open reference architecture for running AI agents at the enterprise level. Last week, we stress-tested it: we took it to a panel at Black Hat and spent about one hour being asked hard questions about it. Play Video: Snyk x Docker x Keycard | Agent Baseline Panel @ Black Hat 2026 The most useful question came from someone who had actually already read it.

Autonomous AI attacks have definitively moved from the research demos everyone's been awed by to standard operating procedure. For anyone paying enough attention, this is not necessarily news: the Five Eyes Alliance warned everyone back in June that AI will bypass cybersecurity in months, not years, with adversary breakout times that can now be measured in seconds. Gartner itself predicted something similar, expecting the window to exploitation to be cut in half as early as next year.

Applications can change several times between scheduled security assessments. New features, APIs, and integrations may introduce risk long before the next annual penetration test begins. That gap is pushing offensive testing beyond a single tool or a single point-in-time engagement. Teams are increasingly combining Dynamic Application Security Testing (DAST), AI penetration testing, and AI red teaming to evaluate different layers of application risk.

When we started thinking about how to surface AI model risk inside Evo, the obvious answer was to borrow from how we score everything else: find the issue, assign a severity, surface it. Done. The core of the new approach is a real risk score, built the way security teams already reason about risk: Likelihood × Impact. Likelihood comes from Attack Success Rate (ASR), the share of real adversarial attacks that succeed against a model. Impact is how much damage the attacker's goal does when it lands.

The Remediation Agent and Malicious Code Defense are the first two pieces of Evo Agentic AppSec: security that not only surfaces risk, but resolves it and prevents the next ones. This morning, we announced the broadest expansion of the Snyk AI Security Platform to date: discover, remediate, validate, and prevent. A loop with a missing segment is not a loop; it is a gap that an autonomous attacker will occupy. Evo Continuous Offensive Security closes validation and shipped today.