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Advanced AI agents (agentic models, which are AI systems that can autonomously test and execute code) emerging in 2026 are compressing the time between discovering a vulnerability and launching attacks from weeks to seconds, making traditional security catalogs obsolete. These AI-powered attackers can now move laterally across converged IT and OT (operational technology, the systems controlling physical infrastructure like factories) networks at machine speed, potentially causing physical damage before humans even detect the breach.

Malware developers are embedding forbidden text about weapons into spyware code to trick AI analysis tools into refusing to analyze it. The malicious code hides real instructions in comments (lines ignored by the computer) and uses obfuscation (making code hard to read) to confuse AI-powered security scanners that feed code directly to language models without treating it as untrusted input.

Researchers demonstrated that a malicious AI agent skill (a reusable tool that extends an AI agent's capabilities) passed security scanners and reached 26,000 users by exploiting a gap in how skills are reviewed. The attack worked by hiding malicious instructions behind a fake website domain that redirected to a legitimate site during security checks, then changed its content after approval to collect user data, showing that one-time security scans cannot detect skills that behave differently after they gain trust.

As organizations increasingly adopt AI across their operations, they face growing security challenges because AI systems touch critical infrastructure and data in complex ways. AI security posture management (AI-SPM, tools that monitor and assess the security of AI models, data, and applications) has emerged as an important category to address these risks, especially since many enterprises run AI agents without proper security oversight or governance strategies. Security teams need to carefully evaluate AI-SPM tools to match their organization's maturity level and avoid duplicating existing security tools or leaving coverage gaps.

AI agents deployed in organizations need access to sensitive systems, but current OAuth tokens (standardized digital credentials that verify identity and permissions) cannot properly track both the agent's identity and the user it represents, making it impossible to enforce proper access controls or detect misuse. The problem grows as agents become more autonomous and can act on behalf of multiple users, invoke other agents, and operate without human oversight. OAuth tokens were designed for single-principal scenarios (one actor), but AI agents operate in complex multi-principal situations that the industry has not yet standardized.

Anthropic's Mythos AI model identified vulnerabilities in classified U.S. government computer systems within hours during a security testing initiative called Project Glasswing, according to a U.S. official. The testing was conducted in cooperation with U.S. intelligence agencies to assess potential security risks the model could pose. In response, the Trump administration issued a directive requiring Anthropic to prevent foreign nationals from accessing its latest models (Fable 5 and Mythos 5), and Anthropic disabled these models for all customers to comply.

Meta paused an employee monitoring program called the Model Compatibility Initiative (MCI) after employees bypassed its security protections to access restricted data, and then did so again even after Meta claimed to fix the vulnerability. The program collected sensitive data including keystrokes, mouse movements, screen content, private conversations, and performance information to train AI models. Security experts criticized Meta for deploying inadequate access controls (security measures that limit who can view data) on such highly sensitive information, even though the company had the resources to implement stronger protections.