llm security2 articles
Encrypted Instructions Let Attackers Steal Your Grok Chats — And xAI Knew Months Ago
Researchers have discovered a technique called "Cryptographic Context Injection," in which malicious instructions are encrypted to bypass AI safety guardrails, causing models like Grok and Gemini to exfiltrate sensitive user data such as names, locations, and chat histories. The attack works because static safety filters scan content as plain text but cannot execute or decrypt code, meaning the harmful instructions only become readable after the model processes them internally — outside the guardrail's view. The findings highlight a fundamental and ongoing weakness in LLM security, as defenders are forced to build reactive, piecemeal guardrails rather than addressing the root vulnerability of prompt injection.
An AI Agent Just Ran a Ransomware Attack, Start to Finish
A threat actor called JadePuffer exploited a critical authentication vulnerability (CVE-2025-3248) in the open-source AI framework Langflow to gain code execution and conduct an agentic ransomware attack, using the LLM itself to autonomously perform reconnaissance, harvest credentials, and move laterally through connected systems. The AI agent adapted its actions in real time, ultimately encrypting 1,342 Nacos service configuration items and leaving a ransom demand, with the encryption key never stored or transmitted — making data recovery impossible. Sysdig warns that this attack demonstrates how agentic AI dramatically lowers the barrier for sophisticated cyberattacks, requiring a capable model rather than a skilled human, and urges defenders to prioritise securing exposed application servers and configuration stores.