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Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation

ICLR 2024 · Large language models › Language, safety and interpretability · approach: Jailbreak attacks and defenses · 11 citations (OpenAlex)

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Shows that simply varying decoding hyperparameters and sampling methods (and removing the system prompt) raises misalignment rates of open-source LLMs from near 0% to over 95%, and proposes generation-aware alignment as a partial defense.

Problem

Open-source LLMs that were safety-aligned before release can be jailbroken; safety evaluations typically use a single fixed default decoding configuration, which may underestimate risk.

Approach

A "generation exploitation" attack that needs no adversarial prompt optimization: it sweeps generation configurations (removing the system prompt, varying temperature, top-K, top-p), then uses a classifier-based scorer to pick the most misaligned response. A complementary generation-aware alignment method fine-tunes on aligned and misaligned outputs collected across decoding strategies.

Key results

• Varying decoding configurations raises ASR on LLaMA2-7B from 14% (greedy with system prompt) to 97% across the full sweep; 9 of 11 models reach at least 95% ASR.
• On LLaMA2-13B-chat, the attack reaches 98% ASR on AdvBench (substring metric) versus 38% for GCG, at roughly 30x lower compute.
• Generation-aware alignment reduces the attack ASR on LLaMA2-7B from 95% to 69%, compared with 88% for fixed-decoding alignment.

Measured effects reported in the paper

Surprising findings

• Removing the system prompt alone raises ASR on LLaMA2-chat models from 0% to 8-16% under greedy decoding, and sampling variations push it much higher.
• Attacks on gpt-3.5-turbo reach only 7% ASR, far below open-source models.

Limitations

• The harmfulness metric is judged by a substring-free classifier trained on HH-RLHF with only 92% human agreement, and the paper calls for better automatic harmfulness metrics.
• The attack assumes the attacker has a scorer trained on the aligned and misaligned output distribution.
• Refined alignment experiments are only run on LLaMA2-7B and the appended "An aligned answer:" string is a simulation device.

Experimental setup

Datasets: AdvBench HH-RLHF

Benchmarks: AdvBench MaliciousInstruct

Metrics: attack success rate harmfulness percentage

Baselines: GCG substring matching evaluation fixed-decoding alignment

Builds on: GCG chain of hindsight context distillation

Closest papers by meaning

• Weak-to-Strong Jailbreaking on Large Language Models (ICML 2025 · similarity 0.92)
• JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models (NeurIPS 2024 · similarity 0.91)
• Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy (CVPR 2025 · similarity 0.91)
• Understanding and Enhancing the Transferability of Jailbreaking Attacks (ICLR 2025 · similarity 0.90)
• One Model Transfer to All: On Robust Jailbreak Prompts Generation against LLMs (ICLR 2025 · similarity 0.90)
• Adversarial Reasoning at Jailbreaking Time (ICML 2025 · similarity 0.90)