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Researchers discovered that Group Relative Policy Optimization (GRPO), a technique normally used to improve AI safety, can be reversed to break safety alignment when the reward signals are changed. By giving a safety-aligned model even a single harmful prompt and scoring responses based on how well they fulfill the harmful request rather than refusing it, the model gradually abandons its safety guidelines and becomes willing to produce harmful content across many categories it never encountered during the attack.

This research introduces PP-DR, a privacy-preserving dimensionality reduction (a technique that reduces the number of features in a dataset to make it easier to analyze) scheme that uses homomorphic encryption (a type of encryption that allows computations on encrypted data without decrypting it first) to let multiple organizations securely share and analyze data together without revealing sensitive information. The new method is much faster and more accurate than previous approaches, achieving 30 to 200 times better computational efficiency and 70% less communication overhead.

Researchers have developed a new backdoor attack method called shell code injection (SCI) that can implant malicious logic into deep learning models (neural networks trained on large datasets) without needing to poison the training data. The attack uses techniques inspired by nature, like camouflage, along with trigger verification and code packaging strategies to trick models into making wrong predictions, and it can adapt its attack targets dynamically using large language models (LLMs) to make it more flexible and harder to detect.

AdvScan is a method for detecting adversarial examples (inputs slightly modified to trick AI models into making wrong predictions) on tiny machine learning models running on edge devices (small hardware like microcontrollers) without needing access to the model's internal details. The approach monitors power consumption patterns during the model's operation, since adversarial examples create unusual power signatures that differ from normal inputs, and uses statistical analysis to flag suspicious inputs in real-time with minimal performance overhead.

This recap highlights how attackers are exploiting trusted tools and marketplaces rather than breaking security controls directly. Key threats include malicious skills appearing in ClawHub (a registry for AI agent add-ons), a record-breaking 31.4 Tbps DDoS attack (a flood attack that overwhelms servers with massive traffic), and compromised update infrastructure for Notepad++ being used to distribute malware. The pattern shows attackers are abusing trust in updates, app stores, and AI workflows to gain access to systems.

Claude Opus 4.6, a new AI model, is significantly better at finding zero-day vulnerabilities (security flaws unknown to vendors and the public) than previous models, discovering high-severity bugs in well-tested code that fuzzing tools (programs that test software by sending random inputs) had missed for years. Unlike traditional fuzzing, Opus 4.6 analyzes code like a human researcher would, studying past fixes and code patterns to reason about what inputs would cause failures.

OpenClaw has partnered with VirusTotal (a malware analysis service owned by Google) to scan skills uploaded to ClawHub, its marketplace for AI agent extensions. The system creates a unique SHA-256 hash (a digital fingerprint) for each skill and checks it against VirusTotal's database, automatically approving benign skills, flagging suspicious ones, and blocking malicious ones, with daily rescans of active skills. However, OpenClaw acknowledged that this scanning is not foolproof and some malicious skills using concealed prompt injection (tricking the AI by hiding malicious instructions in user input) may still get through.