AI Sec Watch

Meta is installing a tool called Model Capability Initiative (MCI) on US employees' computers that records their activity, including mouse movements, clicks, keystrokes, and screenshots from work apps and websites. This recorded data will be used to train Meta's AI agents to perform computer tasks more like humans do, though Meta states the data won't be used to evaluate employee job performance.

InstructLab has a security flaw in its `linux_train.py` script that automatically trusts code from external model sources without verification (trust_remote_code=True). An attacker could trick users into downloading a malicious model from HuggingFace (a popular AI model repository) and running training commands, allowing the attacker to execute arbitrary Python code and take over the entire system.

AI agents (AI systems that can retrieve data, use tools, and perform actions automatically) introduce new security challenges because traditional access control (rules about who can use a system) isn't enough. Google Cloud's Gemini Enterprise Agent Platform offers a centralized control point that provides identity management, access control, policy enforcement, and observability (the ability to see and monitor what's happening) to secure how these agents operate.

Robinhood Ventures Fund I, an investment vehicle that lets regular traders buy into private companies, invested $75 million in OpenAI, the AI company behind ChatGPT. This gives retail investors (non-professional traders) access to ownership stakes in one of the most influential artificial intelligence companies, reflecting growing investor demand for exposure to leading AI firms.

This academic survey article examines how AI is being used to improve security in edge computing (processing data on devices near users rather than in distant data centers), while also exploring the new threats that arise when combining AI with edge systems. The article covers both the security challenges unique to AI-enhanced edge environments and potential approaches to address them, looking toward future developments in this field.

Hackers have infected a legitimate Android payment app called HandyPay with malware (trojanized code, meaning legitimate software modified with malicious additions) to steal NFC data (near field communication, the technology that powers tap-to-pay) and PIN numbers, allowing them to clone payment cards and drain accounts. The attackers likely used generative AI to help create the malware, as evidenced by emoji markers in the code that are typical of AI-generated text. The malware is being distributed through fake websites impersonating a Brazilian lottery and a spoofed Google Play store, targeting Android users in Brazil.

A tool called Claude Mythos discovered 271 security vulnerabilities (weak points that could be exploited) in Firefox, Mozilla's web browser. According to Mozilla, all of these flaws could have also been found by a highly skilled human security researcher, suggesting the AI tool didn't discover anything that experienced humans couldn't find.

On January 31, 2026, researchers found that Moltbook, a social network for AI agents, exposed 35,000 email addresses and 1.5 million agent API tokens because its database was unencrypted, including plaintext third-party credentials like OpenAI API keys. The core risk is a "toxic combination," where an AI agent or integration bridges two or more applications through OAuth grants (permission frameworks allowing apps to access each other) or API connections, and each application owner reviews only their own side, missing the security risks created by the bridge itself.

Anthropic is investigating a claim that unauthorized users accessed Claude Mythos, an advanced AI security tool that the company considers too dangerous to release publicly. The unauthorized access likely occurred through misuse of credentials by someone with legitimate access to Anthropic's systems through a third-party vendor, rather than through a traditional hack (a deliberate attempt to break into a computer system). The incident raises concerns about whether large AI companies can adequately control access to their most powerful models.