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U.S. government officials, including Vice President JD Vance and Treasury Secretary Scott Bessent, met with tech CEOs from companies like Anthropic, OpenAI, Google, and Microsoft to discuss the security of large language models (AI systems trained on large amounts of text data) and how to protect against cyber attacks before Anthropic released its new Mythos model. Anthropic briefed government officials on the model's capabilities, including potential offensive and defensive cybersecurity applications, and emphasized that bringing the government into the conversation early about risks and safety measures was a priority.

PraisonAI Agents has a security flaw where tool approval decisions are cached by tool name only, not by the specific command arguments. Once a user approves the `execute_command` tool (a function that runs shell commands) for any command like `ls -la`, all future shell commands in that session bypass the approval prompt entirely. Combined with the fact that all environment variables (including API keys and credentials) are passed to subprocesses, an LLM agent can silently steal sensitive data without asking permission again.

Anthropic released Claude Mythos Preview, an AI model that can automatically discover vulnerabilities (weaknesses in software) and create working exploits (code that takes advantage of those weaknesses) across operating systems and software products. The company is currently limiting access to a few dozen organizations through Project Glasswing to give defenders time to find and fix weaknesses in their own systems before attackers gain widespread access to the model.

Researchers used LLMs (large language models, AI systems trained on vast text data) and model checking (a technique to verify if software behaves correctly by examining all possible states) to study Android's permission system, which controls what apps can access on your phone. The study involved modeling how this system works, checking if it's secure, and finding ways to exploit it using AI techniques.

This research paper examines how visual explanation techniques can help protect federated learning (a machine learning approach where multiple computers train a model together without sharing raw data) from poisoning attacks (attempts to corrupt the training data or model). The authors propose an enhanced version of LayerCAM (a method that visualizes which parts of an input an AI focuses on), combined with autoencoders (neural networks that compress and reconstruct data), to detect and defend against such attacks.

A cyber-attack on a London pathology company in June 2024 caused widespread hospital disruptions and contributed to a patient's death, highlighting real dangers from digital attacks. The article warns that a new AI release could enable more frequent and severe cyber-attacks by giving attackers powerful hacking capabilities, potentially creating widespread chaos in critical digital systems we depend on.