"LLMs appear to be relatively easy to manipulate, allowing them to completely exceed their limits. This became painfully clear during a session we attended at the first edition of Rocket Fuel Factory Global Sync. The underlying message: thinking like a hacker opens up far more possibilities than you might think. Security vendors and the MSPs and MSSPs that use their tools are lagging far behind in terms of these possibilities."
"On the one hand, there is reinforcement learning based on human feedback. This is what you might call safety training. One of the results of that training is that the model knows what it can and cannot do, the so-called guardrails. You can see this as the conscience of AI or an LLM. Always trying to stay away from harmful or unsafe outputs, suggesting safe alternatives, and consistently refusing to comply with harmful requests."
Large language models can be manipulated to bypass developer-imposed safety restrictions and produce harmful outputs, including assistance in creating large-scale malware. A demonstration at the Rocket Fuel Factory Global Sync showed that adopting a hacker mindset enables far broader exploitation of LLMs than vendors and managed security providers anticipate. Models trained with human-feedback reinforcement produce guardrails that act as a conscience, while in-context learning allows adaptation based on prompts and interactions. Human-like characteristics attributed to LLMs make them susceptible to social-engineering-style manipulation. The discovered vulnerability was reported to Anthropic.
Read at Techzine Global
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