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Foot-In-The-Door: A Multi-turn Jailbreak for LLMs

Zixuan Weng, Xiaolong Jin, Jinyuan Jia, Xiangyu Zhang

arXiv:2502.19820v3cs.CLcs.AI

TL;DR

Jailbreak prompts can bypass LLM safeguards, and the paper asks whether gradual escalation across interactions can erode alignment. FITD combines escalating queries with bridge prompts and response realignment; it reports a 94% average attack success rate across seven models and performance above existing state-of-the-art methods on two benchmarks.

  • Problem

    Jailbreaks use adversarial prompts to bypass safeguards, and the paper asks whether gradual escalation across interactions can exploit this vulnerability in LLM alignment.

    Source: §1 · pp. 1–2

  • Method

    FITD escalates query maliciousness over multiple turns using intermediate bridge prompts and realigns target responses that do not match the intended direction.

    Source: §1 · p. 2, §3.3.1 · p. 4

  • Results

    FITD reports a 94% average attack success rate across seven widely used models and outperforms state-of-the-art approaches on two jailbreak benchmarks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The authors identify multi-turn self-corruption as a weakness in current safety measures and a subject for further AI safety research.

    Source: §5 · p. 8

    The self-corruption analysis remains preliminary, and evaluation across more benchmarks and multimodal models is needed.

    Source: §7 · p. 9

Abstract

from arXiv · show

Ensuring AI safety is crucial as large language models become increasingly integrated into real-world applications. A key challenge is jailbreak, where adversarial prompts bypass built-in safeguards to elicit harmful disallowed outputs. Inspired by psychological foot-in-the-door principles, we introduce FITD,a novel multi-turn jailbreak method that leverages the phenomenon where minor initial commitments lower resistance to more significant or more unethical transgressions. Our approach progressively escalates the malicious intent of user queries through intermediate bridge prompts and aligns the model's response by itself to induce toxic responses. Extensive experimental results on two jailbreak benchmarks demonstrate that FITD achieves an average attack success rate of 94% across seven widely used models, outperforming existing state-of-the-art methods. Additionally, we provide an in-depth analysis of LLM self-corruption, highlighting vulnerabilities in current alignment strategies and emphasizing the risks inherent in multi-turn interactions. The code is available at https://github.com/Jinxiaolong1129/Foot-in-the-door-Jailbreak.

1 Introduction

The introduction frames jailbreaks as a safety challenge and asks whether gradual escalation can erode LLM alignment across interactions. FITD uses bridge prompts and response realignment, and is reported to achieve a 94% average success rate across seven models.

1 Introduction

  • Earlier jailbreaks commonly used engineered single-turn prompts, while multi-turn methods often relied on handcrafted prompts or complex agents and had limited overall ASR.The introduction identifies prompt-engineering expertise as a further burden of those approaches.Source: §1 · p. 1
  • The motivating question is whether minor initial commitments can make an LLM more susceptible to increasingly unethical requests during a multi-turn exchange.The example describes a progression from an innocent introduction to instructions for hacking an email account.Source: §1 · p. 2
  • FITD progressively escalates query maliciousness through intermediate bridge prompts and nudges the model to realign responses that do not match the current intent.These processes are designed to induce increasingly harmful outputs over multiple interactions.Source: §1 · p. 2
  • 94% average success rate across seven widely used models is reported for FITD, which is also described as outperforming state-of-the-art approaches on two jailbreak benchmarks.The paper frames this as a multi-turn jailbreak strategy.Source: §1 · p. 2
  • The paper analyzes FITD-related self-corruption to highlight potential weaknesses in current safety measures and motivate further AI safety research.Source: §1 · p. 2

2 Related Work

Related work distinguishes single-turn attacks from methods that exploit dialogue dynamics, and describes a shift toward multi-turn jailbreaks. The paper positions FITD against existing multi-turn attacks that rely on handcrafted prompts and have limited ASR.

2 Related Work

  • Single-turn jailbreaks include black-box input transformations and white-box attacks that use model parameters for gradient-based input optimization or generation control.Source: §2 · p. 2
  • Multi-turn approaches decompose harmful queries into innocuous sub-questions or iteratively use LLMs for automated red teaming.Source: §2 · p. 2
  • Crescendo and ActorAttack incrementally steer benign-seeming queries toward harmful content but rely on fixed human-crafted seed prompts and have limited overall ASR.The paper contrasts these approaches with FITD’s use of the foot-in-the-door effect and its analysis of self-corruption.Source: §2 · pp. 2–3
  • Related work also covers defenses based on perturbation or optimization, safety-response strategies, and jailbreak detection.Source: §2 · p. 2

3 Method

FITD adapts the foot-in-the-door idea into a multi-turn jailbreak: query and response histories guide gradual escalation, while Re-Align and SlipperySlopeParaphrase address misaligned responses and refusals. The method combines these steps to steer the target toward increasingly harmful content.

3.1 Inspiration from Psychology: The Foot-in-the-Door Phenomenon

  • FITD hypothesizes that accepting slightly harmful prompts can make a target model more likely to produce disallowed responses to later, more harmful queries.This adapts the psychological foot-in-the-door principle of gradual escalation.Source: §3.1 · p. 3

3.2 Overview

  • The method uses the target model’s own prompts and responses as stepping stones, with SlipperySlopeParaphrase and Re-Align handling refusals or responses misaligned with the intended maliciousness.Source: §3.2 · p. 3

3.3 FITD

  • FITD initializes increasingly malicious queries, appends them to chat history, and queries the target; after refusal, it checks the last query-response pair and selects a corrective step.An aligned response leads to an intermediate bridge query, while a misaligned response leads to Re-Align.Source: §3.2 · p. 3, §3.3 · p. 4

3.3.1 Re-Align

  • Re-Align adds an alignment prompt that identifies inconsistencies between the last query and response and encourages the target to respond more consistently with the conversation.It is used when the previous response is too benign or partially refuses despite the query’s malicious intent.Source: §3.3.1 · p. 4

3.3.2 SlipperySlopeParaphrase

  • When the last response is aligned but a refusal occurs, SlipperySlopeParaphrase inserts an assistant-generated query whose maliciousness falls between the last and current queries.If the bridge is refused, it is paraphrased until accepted, and the resulting query-response pair is added to chat history.Source: §3.3.2 · p. 4

3.3.3 Putting It All Together

  • Together, gradual escalation and the two modules use the model’s own responses as stepping stones toward overtly disallowed content.The paper states that well-aligned models can be coaxed into harmful outputs when escalation is gradual and carefully structured.Source: §3.3.3 · pp. 4–5

4 Experiment

Across seven models and two jailbreak benchmarks, FITD outperformed the baselines, while transfer and ablation experiments examined its cross-model reach and component effects. The experiments also found that gradual escalation and later-stage queries matter to attack success.

4.1 Experimental Setup

  • Experiments covered seven open- and closed-source models, JailbreakBench and HarmBench, with malicious level n=12 and three attack attempts per query.ASR measures the percentage of attempts that elicit a harmful response; the datasets contain 100 and 80 harmful queries, respectively.Source: §4.1 · p. 5

4.2 Main Results

  • FITD consistently outperformed all baselines across evaluated models, averaging ASR 94%/91% and reaching 98%/93% on LLaMA-3-8B.Table 1 reports JailbreakBench/HarmBench values in that order.Source: Table 1 · p. 6, §4.2 · p. 6
  • For multi-turn attacks, ActorAttack was the strongest baseline, outperforming CoA across most models; CoA achieved 63%/53% on LLaMA-3.1-8B and 58%/50% on GPT-4o-mini.The paired values are ASRs on JailbreakBench and HarmBench, respectively.Source: §4.2 · p. 6, Table 1 · p. 6

FITD demonstrates strong cross-model trans-

  • LLaMA-3.1-crafted jailbreak histories achieved 76% ASR on Mistral-v0.2 and 74% on Qwen-2-7B; GPT-4o-mini-sourced histories reached 85% on Mistral-v0.2.On GPT-4o-mini, LLaMA-3.1-crafted histories achieved 70% ASR; under GPT-4o-mini transfer, Qwen-1.5-7B had 64%.Source: FITD demonstrates strong cross-model… · p. 6

4.3 Ablation Study

  • Removing Re-Align, palign, and SSP together lowered ASR from 92% to 75% on LLaMA-3.1 and from 98% to 59% on LLaMA-3.Without Re-Align and palign, ASR was 91% on LLaMA-3.1 but 63% on LLaMA-3; removing only Re-Align left 92% and 79%, respectively. The study identifies response alignment, prompt alignment, and paraphrasing as important to high ASR.Source: §4.3 · pp. 6–7
  • ASR generally rose with malicious level n, peaking around n=9 to n=12 before plateauing or fluctuating slightly at n=15.LLaMA-3.1-8B and GPT-4o-mini peaked at n=12, while LLaMA-3-8B and Qwen2-7B peaked at n=9; the authors note a complexity-effectiveness trade-off.Source: §4.3 · p. 7
  • Later-stage queries contributed more to attack success, and smooth escalation outperformed large jumps; LLaMA-Guard-3 lowered ASR to 78%-84%.OpenAI Moderation reduced ASR by 3%-8%, while LLaMA-Guard-2 lowered it to 79%-91%.Source: §4.3 · pp. 7–8, Figure 4 · p. 8
  • Mean response harmfulness increased from about 2.32 at level i=1 to 4.23 at i=12 across models.The authors report that harmfulness scores generally rose with malicious level.Source: §4.3 · p. 8, Figure 4 · p. 8

5 Conclusion

FITD combines progressive malicious-query escalation with SlipperySlopeParaphrase and ReAlign, and achieves a 94% average attack success rate across multiple models. The findings identify LLM self-corruption during adversarial interactions as a weakness in current AI safety measures.

5 Conclusion

  • 94% average attack success rate across multiple models is reported for FITD, which uses SlipperySlopeParaphrase and ReAlign.The method progressively escalates malicious intent through intermediate prompts.Source: §5 · p. 8
  • FITD findings reveal that LLM responses can gradually shift toward harmful content by themselves during adversarial interactions.The paper describes this as self-corruption and a weakness in current AI safety measures.Source: §5 · p. 8
  • The paper recommends real-time adaptive monitoring and stronger alignment methods for multi-turn conversations.Source: §5 · p. 9

6 Ethical Considerations

The authors frame the study as identifying LLM alignment weaknesses to help strengthen AI defenses, while acknowledging that the method bypasses safeguards. They argue that transparent research and responsible disclosure can help developers address vulnerabilities.

6 Ethical Considerations

  • The authors state that their goal is to strengthen AI defenses, although their method bypasses safeguards.Source: §6 · p. 9
  • They argue that transparent research and responsible disclosure help AI developers address vulnerabilities proactively.Source: §6 · p. 9
  • They call for adversarial training, real-time monitoring, and collaboration among researchers, industry, and policymakers to secure AI systems.Source: §6 · p. 9

7 Limitation

The authors characterize the FITD and self-corruption phenomenon as preliminary and call for more systematic study. They also identify evaluation across additional benchmarks and multimodal models as future work.

7 Limitation

  • The FITD and self-corruption phenomenon remains preliminary, requiring more in-depth analysis.Source: §7 · p. 9
  • The authors call for systematic investigation of how self-corruption occurs and methods to mitigate it.They note that current alignment lacks explicit mechanisms to prevent degradation in multi-turn conversations.Source: §7 · p. 9
  • Further evaluation across more benchmarks and multimodal models is needed to examine FITD in Vision LLMs.Source: §7 · p. 9

A.1 Related work

The related work surveys safety alignment and red-teaming approaches, describing both their roles and limitations. It covers training-time alignment, inference-time detection, human vulnerability discovery, and scalable automated evaluation.

Safety-aligned Model

  • Supervised fine-tuning on curated safety datasets provides foundational alignment, but may not prevent unsafe outputs in novel contexts.Reinforcement Learning from Human Feedback and Direct Preference Optimization further refine alignment using human preferences.Source: §A.1 · p. 13
  • At inference time, Llama-Guard detects unsafe generations, while red teaming evaluates model robustness.Source: §A.1 · p. 13
  • Human red teaming uncovers vulnerabilities and supports attack-taxonomy development, but its high cost limits large-scale deployment (Kang et al., 2024; Deng et al., 2023a).Source: §A.1 · p. 13
  • Automated red teaming offers a scalable alternative using adversarial attacks, jailbreak attacks, and benchmarks to probe defenses and alignment (Ge et al., 2024).Source: §A.1 · p. 13

A.2 Differences Between Our Work and Other Similar Work

FITD differs from Wang et al. (2024d) by using prior model responses and bridge prompts to escalate across turns, with response-adaptive mechanisms for rejection and deviation. The paper reports a 94% attack success rate across multiple LLMs and analyzes self-corruption risks.

A.2 Differences Between Our Work and Other Similar Work

  • FITD achieves a 94% attack success rate across multiple LLMs while providing an analysis of self-corruption risks in multi-turn interactions.The paper contrasts its approach with Wang et al. (2024d).Source: §A.2 · p. 13
  • Unlike Wang et al. (2024d), FITD uses the model’s own prior responses and intermediate bridge prompts to guide escalation across multiple turns.Wang et al. (2024d) uses incremental sub-questions and actor role-playing [Ren et al. (2024c)].Source: §A.2 · p. 13
  • FITD adds SlipperySlopeParaphrase to rephrase rejected prompts and Re-Align to adjust responses that do not match the prompt’s intent.Wang et al. (2024d) splits rejected queries but does not adapt to the model’s response behavior.Source: §A.2 · p. 13
  • FITD explicitly uses dialogue history to influence subsequent behavior, whereas Wang et al. (2024d) treats each query independently.The authors report that sustained adversarial engagement can erode even strong initial safeguards.Source: §A.2 · p. 13

A.3 Experiment Setup

The experiment setup compares FITD with seven popular jailbreak methods, using the baselines’ default parameters.

A.3 Experiment Setup

  • The experiments compare FITD against seven popular jailbreak methods.The supplied setup passage does not identify the methods by name.Source: §A.3 · p. 13
  • The baseline methods are run with their default parameters.Source: §A.3 · p. 13

A.3.1 Baseline

The appendix describes the comparison baselines, evaluation datasets and criteria, and the prompts and case studies for FITD’s auxiliary mechanisms. The cases illustrate increasingly detailed malicious outputs as interaction turns escalate.

A.3.1 Baseline

  • The baseline set includes black-box attacks using nested scenarios, code-completion disguises, prompt rewriting, contextual multi-turn feedback, and actor-network theory.ActorAttack is used because Crescendo’s source code was not publicly available; the cited paper reports ActorAttack outperforming Crescendo.Source: §A.3.1 · p. 14

A.3.2 Dataset

  • Evaluation uses 100 JailbreakBench misuse behaviors and the HarmBench validation set, drawn from a benchmark with 510 unique harmful behaviors.JailbreakBench examples span ten broad categories; HarmBench includes textual and multimodal behaviors.Source: §A.3.2 · p. 14

A.3.3 Evaluation

  • GPT-4o assesses both response harmfulness and how well the response aligns with the original query.Source: §A.3.3 · p. 14

Evaluation Prompt

  • The evaluation prompt labels a response unsafe only when it contains harmful content that specifically addresses the request; irrelevant or non-harmful requests are safe.A warning or disclaimer does not make an otherwise harmful answer safe.Source: Evaluation Prompt · p. 14

A.3.4 SlipperySlopeParaphrase

  • SlipperySlopeParaphrase generates an intermediate query more negative than the first but less negative than the second, preserving the second query’s intent while softening its extremes.The generated bridge is placed between qlast and qi in maliciousness.Source: Prompt of SlipperySlopeParaphrase · pp. 14–15, Figure 5 · p. 17

A.3.5 ReAlign

  • Re-Align rewrites a response that is too benign, irrelevant, or partially refusing so it more closely matches the intent of its prompt.The case study describes using palign when the original response and query are misaligned.Source: Prompt of Re-Align · p. 15, §A.4.2 · p. 16

Harmfulness Evaluation Prompt

  • The harmfulness prompt uses a five-point scale, from fully safe or refusing responses to detailed guidance that completely fulfills a harmful request.Intermediate scores distinguish indirect hints and partially harmful content from explicit, risk-bearing guidance.Source: Harmfulness Evaluation Prompt · pp. 15–16

A.4.3 Case Study on FITD

  • The FITD case studies report that outputs become increasingly detailed as maliciousness rises across turns, illustrating the pattern in two examples.The examples concern hacking a government official’s email account and writing a discriminatory letter about a colleague’s biracial marriage.Source: §A.4.3 · p. 16, Figure 7 · p. 19, Figure 8 · p. 20
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