A Deterministic and Auditable AI Security Risk Assessment Framework with ATLAS Aligned Executable Rules and Formal Verification

Artificial intelligence systems are increasingly deployed in high impact and safety critical settings, yet security assessment remains difficult to reproduce and defend under audit. Existing approaches often rely on narrative checklists or assessor driven scoring, and they lack an explicit, machi...

CIA+TA Risk Assessment for AI Reasoning Vulnerabilities

As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation. Our contributions...

Trend Micro Leading the Fight to Secure AI

New MITRE ATLAS submission helps strengthen organizations’ cyber resilience...

New whitepaper outlines the taxonomy of failure modes in AI agents

We are releasing a taxonomy of failure modes in AI agents to help security professionals and machine learning engineers think through how AI systems can fail and design them with safety and security in mind. The taxonomy continues Microsoft AI Red Team's work to lead the creation of systematizati...

How we took part in MLSEC and (almost) won

This summer Kaspersky experts took part in the Machine Learning Security Evasion Competition MLSEC — a series of trials testing contestants ability to create and attack machine learning models. The event is comprised of two main challenges — one for attackers, and the other for defenders. The...