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Attacking Large Language Models with Projected Gradient Descent
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Current LLM alignment methods are readily broken through specifically crafted adversarial prompts. While crafting adversarial prompts using discrete optimization is highly effective, such attacks typically use more than 100,000 LLM calls. This high computational cost makes them unsuitable for, e.g., quantitative analyses and adversarial training. To remedy this, we revisit Projected Gradient Descent (PGD) on the continuously relaxed input prompt. Although previous attempts with ordinary gradient-based attacks largely failed, we show that carefully controlling the error introduced by the continuous relaxation tremendously boosts their efficacy. Our PGD for LLMs is up to one order of magnitude faster than state-of-the-art discrete optimization to achieve the same devastating attack results.
Forward citations
Cited by 8 Pith papers
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On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
Pre-LayerNorm transformers and linear attention are almost always surjective, so any target output has an input that produces it in the continuous embedding space.
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SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
A systemization of LLM jailbreak security that adds linked taxonomies, an evaluation platform, and JailbreakDB, while its main attack–defense comparison results remain deferred.
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FORTRESS: Frontier Risk Evaluation for National Security and Public Safety
A new benchmark with instance-specific rubrics measures frontier LLMs' willingness to assist with national security and public safety threats, alongside a paired over-refusal test.
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SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
A security-aware prompt compressor that reveals the hidden intent of jailbreak prompts and injects it into the system prompt reduces average attack success from 35% to 1% with negligible overhead.
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Position: It's Time to Optimize LLMs for Self-Consistency
The paper proposes self-consistency, a mathematical framework that treats relationships between model outputs across related inputs as the primary training target, unifying many existing alignment and robustness methods.
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Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.
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Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Turn Attacks
CoopGuard's defer-tempt-analyze-coordinate agents cut reported jailbreak success and raise attacker token costs on the new EMRA benchmark, but the deceptive-rate metric is partly defined by the paper's own scoring rubric.
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SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models
Across 510 HarmBench behaviors and seven attack methods, GPT-4 models show more consistent jailbreak resilience than DeepSeek models, whose vulnerability grows with scale.
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