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Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
Open accessAbstract
Recent advances in instruction-following large language models (LLMs) have led to dramatic improvements in a range of NLP tasks. Unfortunately, we find that the same improved capabilities amplify the dual-use risks for malicious purposes of these models. Dual-use is difficult to prevent as instruction-following capabilities now enable standard attacks from computer security. The capabilities of these instruction-following LLMs provide strong economic incentives for dual-use by malicious actors. In particular, we show that instruction-following LLMs can produce targeted malicious content, including hate speech and scams, bypassing in-the-wild defenses implemented by LLM API vendors. Our analysis shows that this content can be generated economically and at cost of $125-500 \times$ cheaper than human effort alone. Together, our findings suggest that LLMs will increasingly attract more sophisticated adversaries and attacks, and addressing these attacks may require new approaches to mitigations.
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“Potential consequences include exfiltrating user data, executing arbitrary code, and more [18, 23, 33, 42].”
From AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents (2024) · cites this paper as [23]
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“Even without actively specifying any social engineering techniques, the model's output could mirror some standard scam persuasion techniques [20].”
From Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection (2023) · cites this paper as [20]
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“However, LLMs also excel at phishing attacks 29 .”
From Human innovation and the creative agency of the world in the age of generative AI (2024) · cites this paper as [29]
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“When prompted, the model may produce convincing personalized scams given appropriate knowledge about the target [21,43] (that was either given in the prompt or, importantly, the model acquired during the chat session).”
From Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection (2023) · cites this paper as [21]
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“Previous work has shown that LLMs can produce convincing scams such as phishing emails [21].”
From Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection (2023) · cites this paper as [21]
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“Different social engineering and persuasion tricks can be automated with LLMs [20] (e.g., claiming authority as an official service, claiming repercussions for not complying such as a loss of the service, distracting the user by implying the requested action is urgent, offering a limited-time kind gesture, etc.).”
From Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection (2023) · cites this paper as [20]
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“Even if applied, it remains unclear whether filtering can be evaded by stronger forms of obfuscation or encoding [21], which future models might further enable.”
From Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection (2023) · cites this paper as [21]
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“A successful attack can allow an external attacker to take actions (and call tools) on behalf of the user.”
From AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents (2024) · cites this paper as [23]
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Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1×
“Our work furthers the study of harms and risks of LLMs (Bender et al., 2021; Bommasani et al., 2021; Liang et al., 2022; Abid et al., 2021; Gehman et al., 2020; Ganguli et al., 2022; Weidinger et al., 2021; 2022), focusing on demonstrating their potential of being actively misused.”
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Training Language Models to Follow Instructions with Human Feedback
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On the Opportunities and Risks of Foundation Models
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Self-citationFinetuned Language Models Are Zero-Shot Learners
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RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
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Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612
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You, I., & Yim, K. (2010) · 2010 International Conference on Broadband, Wireless Computing, Communication and Applications · doi:10.1109/bwcca.2010.85 · Cited by 607
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Cited by
Sorted by citations
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083
Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023) · Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3605764.3623985 · Cited by 667
Security and Privacy Challenges of Large Language Models: A Survey
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Multi-step Jailbreaking Privacy Attacks on ChatGPT
Li, H., Guo, D., Fan, W., Xu, M., Huang, J., Meng, F., & Song, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.272 · Cited by 454
Metadata
- Authors
- Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., & Hashimoto, T.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Stanford University (US)
- Berkeley
- Venue
- 2024 IEEE Security and Privacy Workshops (SPW)
- Publisher
- IEEE
- Published
- 2024-05-23
- Pages
- 132-143
- OpenAlex
- W4400315206
- Cited by
- 114
- References
- 45
- Citations by year
- 2026: 29 · 2025: 41 · 2024: 30
- FWCI
- 29.51
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Security and Privacy Workshops (SPW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Open Philanthropy Project
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- large language models, dual-use risks, hate speech, scams, instruction following, security attacks
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