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How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, Weiyan Shi
TL;DR
Existing AI safety work has largely treated LLMs as machines or instruction followers, leaving persuasion in everyday human-like communication underexplored. The paper develops a social-science-based taxonomy and uses it to generate persuasive adversarial prompts, finding over 92% attack success on several major models and a gap in existing defenses.
Problem
Existing jailbreak research focuses on algorithmic attacks and overlooks risks from natural, human-like communication with everyday users.
Method
The paper proposes a social-science-based persuasion taxonomy and uses it to automatically generate interpretable persuasive adversarial prompts at scale.
Results
Over 92% attack success is reported on Llama-2 7b Chat, GPT-3.5, and GPT-4, while existing post-hoc defenses show a significant effectiveness gap against PAP.
Takeaways & Limitations
The findings highlight jailbreak risks rooted in natural communication and motivate more fundamental AI-safety mitigation for highly interactive LLMs.
Takeaways & Limitations
The study mainly evaluates single-turn persuasion, leaving the effectiveness of multi-turn strategies unclear.
Abstract
from arXiv · showhide
Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak performance across all risk categories: PAP consistently achieves an attack success rate of over $92\%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses, and advocate for more fundamental mitigation for highly interactive LLMs
1 Introduction
The paper reframes jailbreak risk around human-like persuasion, addressing gaps left by algorithm-focused and instruction-following attacks. It introduces a taxonomy-guided method for generating persuasive adversarial prompts and reports strong attack performance against major LLMs.
- Traditional safety research focuses on optimization-, side-channel-, and distribution-based jailbreaks that often produce hard-to-interpret prompts.
- Everyday users may persuade LLMs through natural communication, including emotional appeals that have elicited harmful information.The paper cites the “grandma exploit” as an example involving a bomb-making recipe.
- The paper introduces a taxonomy-guided approach that automatically generates human-readable persuasive adversarial prompts to study human-like communication risks.
- The study develops a persuasion taxonomy, builds a scalable Persuasive Paraphraser, scans 14 risk categories, and conducts an in-depth iterative probe.
- Over 92% attack success is reported on Llama-2 7b Chat, GPT-3.5, and GPT-4 without specialized optimization.
- The paper identifies overlooked risks from natural communication and advocates continued research into vulnerabilities rooted in human-like interaction.
2 Related Work
Prior jailbreak research is organized around optimization, side-channel communication, and distribution-based methods. The paper positions PAP as a higher level of humanization that treats LLMs as human-like communicators rather than algorithmic systems or mere instruction followers.
- Jailbreak methods are broadly classified into optimization, side-channel communication, and distribution-based approaches.
- Optimization: Optimization methods manipulate inputs through gradients, genetic algorithms, or related procedures to elicit harmful-task compliance.
- PAP adds a third humanization level by weaving interpretable persuasion techniques into jailbreak construction for human-like communication.
- Side-channel Communication: Side-channel methods exploit long-tailed distributions through ciphers, low-resource languages, code injection, or virtualization.
- Distribution-based Methods: Distribution-based methods learn from successful manually crafted jailbreak templates and generate variants.
- Humanizing AI also raises risks from unintended interactions, including reported associations between chatbot conversations and a user’s suicide.
3 Persuasion Taxonomy
The paper builds a social-science-based persuasion taxonomy and uses it to generate persuasive adversarial prompts at scale. Its pipeline trains a Persuasive Paraphraser, generates prompts for new harmful queries, and evaluates target-model outputs.
- The taxonomy classifies 40 persuasion techniques into 13 broad strategies using research from psychology, communication, sociology, marketing, and NLP.
- The taxonomy records each technique’s name, definition, and concrete application example, providing the foundation for automated jailbreak generation.
- Persuasive Paraphraser Training: The method gathers 100–230 PAP examples by applying taxonomy techniques to plain harmful queries, using sources such as prompting, prior successful PAPs, or human experts.
- Persuasive Paraphraser Deployment: A fine-tuned Persuasive Paraphraser converts new plain harmful queries and specified persuasion techniques into corresponding PAPs.
- Persuasive Paraphraser Training: Each GPT-3.5 fine-tuning example contains a plain query, a persuasion-technique name, and a sampled PAP applying that technique.
- Persuasive Paraphraser Deployment: A GPT-4 Judge evaluates resultant target-model outputs for harmfulness on a 1 to 5 Likert scale using context, policies, a rubric, and chain-of-thought reasoning.
5 Jailbreak Study I: Broad Scan
The broad scan evaluates 40 persuasion techniques across 14 harmful-risk categories on GPT-3.5, showing that human-readable PAPs can bypass guardrails across the benchmark. Effectiveness varies by risk category and technique, while the study remains limited to single-strategy, one-turn prompts.
- Jailbreak Settings: 40 persuasion techniques were scanned across 14 risk categories using categorized harmful queries on GPT-3.5.The broad scan used three harmful queries per risk category and generated PAP variants for each query-technique pair.
- Jailbreak Settings: 92.9% of generated PAPs accurately applied the intended persuasion technique, with fewer than 10% overlapping other social-engineering methods.The authors use this as evidence that the generation method produces scalable, distinct, human-readable PAPs.
- Broad Scan Results: GPT-3.5 blocked plain harmful queries but did not reliably block paraphrased PAPs expressing the same harmful intent.The broad scan therefore tests whether persuasion changes jailbreak outcomes without changing the underlying harmful request.
- Broad Scan Results: Fraud/deception and illegal activity were the most susceptible risk categories, whereas no category was entirely safe under PAPs.The authors attribute differences possibly to how subtly or clearly categories are defined, while preserving this as a proposed explanation.
- Broad Scan Results: Logical appeal and authority endorsement exceeded 60% effectiveness for certain categories, while threats were generally less effective.Technique effectiveness also depended on the risk category: logical appeal and negative emotional appeal performed differently across unauthorized legal practice and political campaigning.
- Limitations: The study focuses on single-strategy, one-turn PAPs, although real persuasion may involve multiple techniques across multi-turn dialogue.The paper identifies increasingly complex persuasive interactions as a direction for further investigation.
6 Jailbreak Study II: In-depth Iterative Probe
The in-depth probe models iterative refinement by training a Persuasive Paraphraser on successful PAPs and deploying techniques across several aligned LLMs. PAPs achieved over 92% ASR on Llama-2 and GPT models within 10 trials and outperformed algorithm-focused baselines, while susceptibility varied by model family.
- Jailbreak Settings: The in-depth probe trains a Persuasive Paraphraser on 230 successful PAPs and iteratively applies persuasion techniques until a jailbreak is detected.The procedure models bad actors refining effective prompts across techniques and sessions.
- Jailbreak Settings: Five aligned LLMs were tested with PAPs using the AdvBench benchmark of 50 distinct harmful queries.The target models were Llama-2 7b Chat, GPT-3.5, GPT-4, Claude 1, and Claude 2.
- Qualitative Evaluation: Qualitative examples show PAPs using techniques such as non-expert testimonial and misrepresentation to elicit harmful content after guardrails block the original query.The examples are presented for GPT-3.5 and GPT-4, with sensitive content redacted in the reported evaluation.
- PAP Comparison with Baselines: PAP was more effective than representative baseline attacks on Llama-2, GPT-3.5, and GPT-4 without specialized optimization.The paper notes that GCG achieved comparable ASR with GPT-3.5 but required more computational resources.
- PAP Performance Across Trials: GPT-4 was more susceptible to PAPs than GPT-3.5 in early trials despite being the more capable model.The paper proposes that stronger models may better understand and respond to persuasion, distinguishing PAP risk from prior patterns favoring smaller-model attacks.
- PAP Performance Across Trials: Over 92% ASR was achieved on Llama-2 and GPT models within 10 trials, while performance was much more limited on Claude models.The authors report that Claude was harder to jailbreak and associate this robustness with its use of RLAIF as a possible factor.
7 Re-evaluating Existing Defenses
The defense evaluation compares mutation-based and detection-based post-hoc defenses against PAPs. Mutation methods generally reduce ASR more, but effectiveness declines on more capable models and remains insufficient on GPT-4.
- Defense Strategies: The evaluation examines mutation-based and detection-based post-hoc defenses without modifying the base model or initial system settings.Perplexity-based defenses are omitted because they depend on identifying unusually high perplexity.
- Defense Results: Mutation-based defenses outperform detection-based defenses in lowering PAP ASR overall.The tested mutation methods include Rephrase and Retokenize; detection methods include Rand-Drop, RAIN, Rand-Insert, Rand-Swap, and Rand-Patch.
- Defense Results: Mutation defenses can alter benign queries and potentially diminish model helpfulness.This creates a trade-off between reducing harmful outputs and preserving the meaning and utility of benign inputs.
- Defense Results: 60% ASR remained on GPT-4 after the most effective defense, exceeding the best baseline attack’s 54% ASR.The paper uses this result to argue that current defenses are inadequate for more capable models.
- Implications: Existing defenses often assume gibberish-like attacks and overlook semantic content, leaving subtle human-like communication risks insufficiently addressed.The authors call for threat models and safeguards that encompass nuanced persuasive vulnerabilities.
8 Exploring Adaptive Defenses
The paper evaluates adaptive defenses against persuasive adversarial prompts (PAP), finding that semantic summarization and system-prompt adaptation outperform surface-level mutations, while safety gains can reduce utility.
- Defense mechanisms: Traditional Paraphrase and Retokenize defenses are ineffective against PAP because they do not significantly reduce persuasiveness.Qualitative GPT-4 results show that these surface-level transformations fail to prevent detailed dangerous responses.
- Defense mechanisms: Adaptive defenses target PAPs through system-prompt changes or summarization rather than relying solely on surface-level prompt mutations.The evaluated defenses include an explicit resistance-to-persuasion system prompt, base summarization, and tuned summarization; perplexity-based defenses are omitted because PAPs are coherent and low-perplexity.
- Defense results: The tuned summarizer reduces GPT-4 PAP attack success rate from 92% to 2%.It summarizes the persuasive prompt to its core harmful request, triggering the model’s inherent guardrail.
- Defense results: Adaptive defenses also reduce other attacks: system-prompt adaptation lowers GPT-3.5 GCG ASR from 86% to 0%, while tuned summarization reduces PAIR and GCG ASR below 8%.These results suggest that defenses developed for PAP can transfer across adversarial prompt types.
- Trade-offs and scope: Defense selection involves a safety–utility trade-off that varies by model.For GPT-3.5, tuned summarization lowers PAP ASR from 94% to 4% but reduces MT-bench from 8.97 to 6.65, whereas Adapt Sys. changes MT-bench from 8.97 to 8.85.
- Trade-offs and scope: The authors argue that model-specific defenses should account for model characteristics and threat type rather than use one universal method.Even models resistant to PAP may remain vulnerable to other jailbreaks, such as complex virtualization attacks affecting Claude.
9 Conclusion
The conclusion reframes jailbreak risk around human-like persuasion rather than only algorithmic attacks. It reports strong PAP performance, identifies weaknesses in existing defenses, and points to adaptive defenses as relevant beyond PAP.
- Conclusion: The paper humanizes LLMs by studying whether persuasive communication can induce them to jailbreak themselves.This perspective contrasts with treating models as algorithmic systems or mere instruction followers.
- Conclusion: The persuasion taxonomy enables systematic generation of persuasive adversarial prompts and links social science research with AI safety.The taxonomy is also presented as a resource for NLP and computational social science.
- Conclusion: PAP achieves an attack success rate above 92% on Llama-2 7b Chat, GPT-3.5, and GPT-4, consistently outperforming algorithm-focused jailbreak methods.The conclusion also reports that more advanced models are more susceptible to PAP while resisting conventional defenses more strongly.
- Conclusion: Adaptive defenses designed for PAP also work against other adversarial attacks, suggesting a connection between persuasion and broader jailbreak risks.The authors identify this connection as a potential direction for more fundamental defenses against persuasion.
Ethical Consideration
The paper frames its disclosure and defense work as intended to strengthen LLM safety while limiting misuse. It also identifies practical constraints on widespread attacks and notes that disclosed PAPs may no longer remain effective.
- Risk boundary: Widespread attacks by untrained users are assessed as relatively unlikely because producing successful PAPs at scale remains difficult for the general population.The authors specifically identify persuasion and paraphrasing harmful queries as challenging without training.
- Responsible disclosure: The study reports disclosure to Meta and OpenAI before publication and restricts PAP fine-tuning details to approved certified researchers.The authors also withhold the trained Persuasive Paraphraser and provide access to jailbreak data only after review.
- Risk boundary: The authors note that PAPs may no longer be effective after disclosure and report Claude’s resistance as one mitigation example.They also propose adaptive system prompts and summarization-based defenses as promising mitigations.
- Responsible disclosure: The authors describe their goal as strengthening LLM safety rather than enabling malicious use.They commit to ongoing monitoring and updating in line with technological advances.
Limitation and Future Work
The paper identifies limitations in its single-turn design, harmfulness evaluation, and technique sampling, while outlining future work on interactive persuasion and model-specific defenses. It also documents unstable simple prompting and the resulting need for more reliable PAP generation.
- Limitations: Single-turn experiments leave the effectiveness of multi-turn techniques such as “foot in the door” and “reciprocity” unresolved.The authors note that multi-turn persuasion may become more effective or may encounter greater resistance after prior rejections.
- Limitations: Equal PAP sampling per technique may not reflect real-world jailbreak distributions, where some techniques are more popular and users may combine techniques.The paper identifies technique popularity and mixed-technique messages as factors that could change the observed distribution.
- Future Work: The study leaves human reactions to PAPs and the real-world harmfulness of post-jailbreak outputs as open questions.The paper also notes that information harmfulness can depend on who accesses it and how it is used.
- Future Work: More sophisticated LLMs may respond more actively to persuasive jailbreaks, motivating research on protections against manipulation.The authors propose examining advanced model interactions with persuasion from cognitive and anthropological perspectives.
- PAP Generation: Simple prompting sometimes changes a harmful query’s intent because of safety guardrails, making PAP generation unstable for scalable experiments.The paper therefore fine-tunes a dedicated Persuasive Paraphraser to generate PAPs more reliably.
D Implementation Details of Defense via Summarization (Base & Tuned Smry.)
The defense study compares a base summarizer with a fine-tuned summarizer that is trained on PAP distributions, then evaluates protection against persuasive and other jailbreak templates.
- Base Smry.: Base Smry. asks GPT-4 to summarize an inquiry before sending the summary to target models for ASR and MT-bench evaluation.The downstream evaluation determines both jailbreak success and helpfulness.
- Tuned Smry.: Fine-tuned Smry. inserts plain harmful queries and corresponding PAPs into a system prompt to model a defender aware of PAP distributions.The fine-tuning pool randomly selects 50 samples from the 230-sample pool used for the persuasive paraphrase model.
- Additional Evaluation: The study also tests Claude models against manually crafted virtualization prompts and uses the fine-tuned summarizer as defense.The evaluation uses 77 jailbreak templates, plus mutated and rephrased variants.
- Results: Summarization shows promise against unseen manually crafted, GPTFuzzer, and Masterkey-derived attacks, with content moderation identified as an important factor.The tuned summarizer learned from persuasive prompts but was evaluated on attacks outside that training distribution.
E.1 Quantitative Analysis of PAPs
The quantitative analysis evaluates whether PAPs apply their assigned persuasion techniques faithfully, preserve harmful intent, and overlap with other jailbreak styles.
- Evaluation Setup: Three human experts annotate PAP quality, beginning with shared examples and then separately labeling 80 PAPs each.The final annotation set contains 240 PAP samples, representing two examples for each of 40 techniques.
- Technique Fidelity: 92.92% of PAPs correctly apply the required persuasion techniques.The result comes from human evaluation of PAPs generated using few-shot fine-tuning.
- Attack Overlap: PAPs overlap by ≤10% with all other evaluated attack types, indicating a distinct risk profile in the reported analysis.The paper notes that persona or virtualization elements can nevertheless arise organically within persuasion.
E.2 Examples (Harmful Contents Warning!)
The examples present PAPs and target-model responses across multiple harmful risk categories, including cases where models refuse requests and cases used to illustrate jailbreak behavior.
- Presentation Scope: The qualitative section truncates jailbreak cases to harmful-information portions and redacts sensitive details; the Child Harm category is omitted.Examples are presented for research purposes despite their potentially harmful content.
- Risk Categories: Examples span illegal activity, hate or violence, malware, physical harm, economic harm, fraud, political campaigning, privacy, legal, financial, medical, and government-decision risks.The figures label broad-scan examples for these risk categories.
- Persuasive Framing: The examples include PAP-style framing that presents harmful requests as prevention, academic research, or open discussion.One fraud example frames scam details as necessary to protect vulnerable individuals, while a smuggling example invokes theoretical understanding for prevention.
- Model Responses: Some target-model responses refuse harmful requests, including requests involving misinformation, personalized financial advice, homemade cancer treatment, anorexia, smuggling, and scams.The refusal examples explicitly cite ethical, safety, or professional-boundary reasons.