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the claim
Adversarial prompts can jailbreak aligned large language models into bypassing their safety training.
the verdict
INSUFFICIENT LEANING
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10 sources for · 0 against
Is it true that Adversarial prompts can jailbreak aligned large language models into bypassing their safety training?
Unclear. Multiple peer-reviewed papers state that aligned large language models remain vulnerable to adversarial prompt jailbreaks, and several demonstrate specific attack methods that bypass safety guardrails. However, each item addresses a particular attack technique or assumes the vulnerability as background rather than settling the general claim outright.
Verdict: PARTIAL LEAN · 10 sources for, 0 against · Last checked
Evidence for · 10
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Visual Adversarial Examples Jailbreak Aligned Large Language Models. (n.d.). cir.nii.ac.jp. https://doi.org/10.1609/aaai.v38i19.30150
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More for · 9
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AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models. (n.d.). openreview.net. https://openreview.net/forum?id=7Jwpw4qKkb
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Rasheed, A. S. A. & Masud, M. M. (2026). Effective Defense Strategies Against Jailbreaking in Large Language Models. doi.org. https://doi.org/10.1109/access.2026.3679168
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
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Zhu, S., Zhang, R., An, B., Wu, G., Barrow, J., Wang, Z., Huang, F., Nenkova, A., & Sun, T. (2023). AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models..
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Karkevandi, M. B., Vishwamitra, N., & Najafirad, P. (2024). Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large Language Models?. doi.org. https://doi.org/10.48550/arXiv.2408.02651
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RECAP: A Resource-Efficient Method for Adversarial Prompting in Large Language Models. (n.d.). arxiv.org. https://arxiv.org/html/2601.15331v1
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Wang, P., Li, H., Lin, H., Lin, W., Wu, F., & Xie, N. (2025). A Hybrid Perplexity-MAS Framework for Proactive Jailbreak Attack Detection in Large Language Models. doi.org. https://doi.org/10.3390/app152413190
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Addepalli, S., Varun, Y., Suggala, A., Shanmugam, K., & Jain, P. (2024). Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?. doi.org. https://doi.org/10.48550/arXiv.2412.03235
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Y, D., R, M., Y, Z., S, S., T, Z., C, W., G, J., Y, Q., J, H., J, M., S, B., & X, H. (2025). Safeguarding large language models: a survey.. doi.org. https://doi.org/10.1007/s10462-025-11389-2
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Y, C., MH, H., PC, J., YC, L., & CC, C. (2026). Clinical Plausibility in Large Language Model Robustness Testing for Medicine: A Scoping Review.. doi.org. https://doi.org/10.1007/s10916-026-02405-1
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