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Researchers at Google DeepMind have identified a vulnerability called 'AI Agent Traps' that allows attackers to manipulate and exploit AI agents (autonomous programs that can browse the web and take actions) by hosting malicious web content designed to deceive them. This research maps out how these attacks work against AI systems that visit websites.

Healthcare workers are increasingly using AI tools on their own to handle heavy workloads, and organizations cannot stop this trend. The source emphasizes that healthcare organizations should strengthen their security practices to reduce the damage if these unsanctioned AI tools are compromised or misused.

OWASP (Open Web Application Security Project, a standards group for security best practices) has updated its generative AI security guidance to address 21 identified risks in AI systems. The update recommends that companies use separate but coordinated defense strategies tailored specifically for generative AI (AI that creates text, images, or code) and agentic AI (AI that can take actions independently).

MaliVD is a deep learning method that detects vulnerabilities (security flaws) in source code and identifies exactly which lines contain them, using a multi-modal attention mechanism (a technique that lets the AI focus on important parts of code by analyzing it in multiple ways, like looking at the code's sequence, tree structure, and relationships between components). Traditional security tools create too many false alarms and struggle with complex modern software, but MaliVD performs better than eight other detection methods by extracting different types of code features and prioritizing suspicious sections.

Researchers discovered that the SAE J1772 charging control protocol, which manages communication between electric vehicle chargers and cars, lacks proper authentication (verification that devices are who they claim to be). They created ChargeX, a hardware attack that modifies charging control signals to disrupt charging schedules, cause denial of service (DoS, making systems unavailable), or damage batteries. Tests on Tesla chargers and home chargers showed the attacks can force unwanted charging states or crash chargers into error states.

This research paper presents VM-DSSE-FB, a new encryption method for cloud storage that lets multiple users search and update encrypted data while maintaining privacy. The system uses techniques like symmetric encryption (encoding data with a shared secret key), homomorphic addition (performing calculations on encrypted data without decrypting it first), and bitmap indexing (a method for organizing data searches) to protect against certain attacks and verify that search results are accurate and complete.

OpenAI is launching a Safety Fellowship program (September 2026 to February 2027) for external researchers to conduct independent studies on safety and alignment (making sure AI systems behave as intended and don't cause harm) of advanced AI systems. Fellows will work on topics like safety evaluation, ethics, robustness, privacy protection, and oversight of AI agents, receiving mentorship, compute resources, and a monthly stipend while producing research outputs like papers or datasets.

Commercial off-the-shelf software (COTS, meaning ready-made software products sold online or in stores) initially seems attractive because it deploys quickly and costs less than custom development, but organizations often get trapped when they want to switch platforms, as their systems become deeply entangled with the vendor's technology. AI-powered security tools are creating a new type of lock-in by relying on proprietary training data, vendor-specific threat intelligence feeds (collections of indicators showing cyber attacks), and specialized hardware, making it expensive and difficult to migrate away.

Fortinet FortiClient EMS has an improper access control vulnerability (a flaw in how the software checks who is allowed to do what) that lets unauthenticated attackers (people without login credentials) run unauthorized code or commands through specially crafted requests. This vulnerability is actively being exploited by attackers in real-world attacks.