AI Sec Watch

Cloud computing systems face side-channel attacks (SCAs, where attackers exploit information leaked through physical resources like CPU cache to compromise VMs on shared servers) when multiple VMs run on the same physical machine. This paper proposes a framework that analyzes user behavior patterns to intelligently place VMs across servers and then continuously moves them around to prevent attackers from keeping their malicious VM next to a target VM long enough to launch an attack, achieving up to 25% reduction in attack risk while improving resource efficiency.

This research proposes a framework for authenticating people based on their faces while protecting their facial privacy in systems like smart building access. The system uses homomorphic encryption (a technique that lets computers perform calculations on encrypted data without decrypting it first) and a multi-party secure authentication process, where multiple parties verify identity together rather than relying on a single trusted server, to prevent privacy breaches and single points of failure.

Knowledge graph embedding (KGE, a technique that converts graph structures into numerical representations for AI tasks) systems can be attacked by deleting or adding false information, which corrupts their learned representations. This paper introduces PathAttack, a new attack method that uses reasoning paths (multi-hop connections between entities) to identify which pieces of data to attack without needing to know targets in advance, showing 5% improvement in attack effectiveness on test datasets.

The Pentagon's chief technology officer stated that Anthropic remains classified as a supply chain risk (a designation meaning the company's technology threatens U.S. national security), but Anthropic's Mythos AI model, which has advanced capabilities for finding and fixing cyber vulnerabilities, is being treated as a separate urgent national security issue requiring the Department of Defense to strengthen its networks. The DOD has blacklisted Anthropic from working with defense contractors, though the agency is reportedly using Mythos internally and is open to negotiations about safeguards (called guardrails, or restrictions on how the AI can be used) if Anthropic agrees to terms similar to those negotiated with other AI companies.

Goodfire, a San Francisco startup, released Silico, a tool that uses mechanistic interpretability (a technique for understanding how AI models work by mapping their internal neurons and connections) to let researchers see inside AI models and adjust their parameters during training. The tool aims to give developers more control over AI behavior by exposing internal 'knobs and dials' so they can reduce unwanted outputs, making AI development more like traditional software engineering rather than trial-and-error.

CISA and international cybersecurity partners released guidance for organizations adopting agentic AI (AI systems that can take actions autonomously on behalf of users). The guidance identifies security challenges with these systems and provides steps for safely designing, deploying, and operating them while connecting AI risk management to existing cybersecurity practices.

Microsoft is launching a new AI agent within Word that is designed specifically for legal teams to help with tasks like reviewing contracts and managing document edits. Unlike general AI models, the Legal Agent follows structured workflows (predetermined sets of steps) based on actual legal practices, handling specific repeatable tasks like reviewing contract clauses against a predefined playbook (a set of rules or guidelines).

Organizations often use AI models from online repositories like HuggingFace without tracking their changes, verifications, or vulnerabilities, which can lead to security risks if models are poisoned (containing hidden malicious code) or contain training biases. Cisco released the Model Provenance Kit, an open source Python-based tool that creates a unique 'fingerprint' for each model using metadata and other signals, allowing organizations to compare models and trace their origins to address these tracking and accountability problems.

AI is changing how software is developed by affecting coding practices, tools, developer roles, and the overall development process across all stages, from initial planning through maintenance. The article discusses how AI agents are being integrated throughout the software development life cycle (the complete process of creating and maintaining software, from concept to deployment).