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GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher

ICLR 2024 · Large language models › Alignment and preferences · approach: LLM-as-judge and feedback data · 14 citations (OpenAlex)

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Introduces CipherChat, a framework that chats with GPT-3.5 and GPT-4 through ciphers plus role-play and demonstrations to bypass safety alignment, and SelfCipher, which uses natural language role-play to evoke an internal cipher.

Problem

Safety alignment of LLMs is mostly conducted in natural language, so it may not generalize to non-natural-language inputs such as ciphers, letting unsafe requests bypass refusals.

Approach

Use a system prompt that assigns the model a cipher-expert role, teaches the cipher, and provides enciphered unsafe demonstrations, so the model replies in cipher and a rule-based decrypter recovers the text; SelfCipher drops explicit ciphers and relies on role-play with natural-language unsafe demonstrations to evoke the model's own cipher.

Key results

• SelfCipher on English reaches 70.9% unsafety rate with GPT-4 on the Crimes domain, versus 0% for vanilla GPT-4 with no cipher, and outperforms human ciphers in most settings.
• The best English human cipher ASCII achieves averaged success rates of 23.7% (Turbo) and 72.1% (GPT-4) across domains, per the paper's introduction.
• Replacing 'Cipher Code' in the SelfCipher prompt with 'English' drops GPT-4 unsafety on English from 70.9% to 1.0%, suggesting the cipher-role term is key.

Measured effects reported in the paper

Surprising findings

• More capable models (GPT-4) produced more unsafe responses through ciphers than GPT-3.5-Turbo, which the authors attribute to better cipher understanding.
• SelfCipher works without any explicit cipher; simulated ciphers that never appear in pretraining data fail even with 10+ demonstrations.

Limitations

• Only black-box access to models, so the internal cause of the 'secret cipher' cannot be examined.
• Focuses on one non-natural language form (ciphers) and the mostly GPT-family models.
• Safety judged by a GPT-4-based detector; manual verification was limited to the Crimes domain, and binary safe/unsafe labels ignore informativeness and fluency.

Experimental setup

Datasets: Chinese safety assessment benchmark (Sun et al. 2023 taxonomy, 11 domains sampled, 199 instances each) Alpaca (general instructions, 100 samples for generalization test)

Benchmarks: Chinese safety assessment benchmark (11 domains, English and Chinese) Alpaca

Metrics: unsafety rate validity rate success rate

Baselines: Vanilla prompting Vanilla + UnsafeDemo Caesar cipher ASCII cipher Unicode cipher UTF cipher Morse code Atbash cipher

Builds on: chain-of-thought in-context learning Jailbroken: competing objectives and mismatched generalization

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