Anthropic’s team of researchers at the AI company, owner of the chatbot Claude, have released a report on vulnerabilities in large language models (LLM) that can lead to inappropriate or dangerous responses, despite developers implementing safeguards. The reported vulnerability stems from continuous question-answer conversations with an LLM, leading to in-context learning of the content, narrowing the range of topics of interest and prompting the LLM to respond inappropriately or dangerously.
In testing, researchers found that when asking the LLM about bomb-making techniques, immediate rejection occurs, but subtle questions like lock picking or cheating money led the LLM to gradually answer bomb-making queries. The study revealed that framing questions within a specific context to the LLM repeatedly, even on general knowledge, resulted in deteriorating response quality over time.
So far, there is no foolproof method to mitigate these vulnerabilities. Limiting the number of conversations to prevent the LLM from reaching a point of dangerous responses may impact regular user experience. Another approach is to continually adapt the model in every successive question-answer interaction, yet this only delays the acceptance of risky questions.
Anthropic’s research team states that the reason for disclosing these vulnerabilities, previously notified to AI LLM developers, is to ensure the developer community understands these dangerous flaws and collaborates on finding solutions.
TLDR: Anthropic’s AI research team highlights a vulnerability in large language models, leading to inappropriate or dangerous responses due to in-context learning, necessitating collaborative efforts to address the issue.
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