If at first you don’t succeed, prompt, prompt again. In a Nov. 5 report, Cisco showed that open-weight large language models—those with their trained parameters publicly available—were especially susceptible to a chain of malicious prompts known as a multi-turn attack. Cisco used its “AI Defense” assessment tool to determine that multi-turn scenarios were two to 10 times more successful than single-turn ones at achieving a cyberattacker’s aims. Tested threats included nefarious tasks like malicious code generation and sensitive information disclosure. The models studied models in the research included Alibaba’s Qwen3-32B, DeepSeek’s v3.1, Google’s Gemma 3-1B-IT, Meta’s Llama 3.3-70B-Instruct, Microsoft’s Phi-4, Mistral’s Large-2, OpenAI’s GPT-OSS-20b, and Zhipu AI’s GLM 4.5-Air. Read about how LLMs can fall apart under questioning.—BH |