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arxiv: 2311.03191 · v5 · pith:5FX652FTnew · submitted 2023-11-06 · 💻 cs.LG · cs.CR

DeepInception: Hypnotize Large Language Model to Be Jailbreaker

Xuan Li , Zhanke Zhou , Jianing Zhu , Jiangchao Yao , Tongliang Liu , Bo Han This is my paper

Pith reviewed 2026-05-19 10:32 UTC · model grok-4.3

classification 💻 cs.LG cs.CR
keywords jailbreakinglarge language modelssafety alignmentadversarial attackspersonificationnested scenariosrole-playingharmful content generation
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The pith

Nested role-play scenes let large language models generate harmful content and keep doing so in later exchanges.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a lightweight technique that gets LLMs to imagine themselves inside a layered fictional scenario built from personification. This construction lets the model treat harmful requests as part of an inner story rather than direct instructions that violate its rules. The resulting outputs show higher rates of harmful content than earlier methods and the effect carries over into follow-up messages without new prompting. The same pattern appears across both open-source models such as Llama-2 and Llama-3 and closed-source ones such as the GPT series.

Core claim

By constructing a virtual nested scene through personification, the method enables LLMs to adaptively escape usage controls in normal scenarios, achieving leading harmfulness rates and sustaining jailbreaks in subsequent interactions on models including Llama-2, Llama-3, GPT-3.5, GPT-4, and GPT-4o.

What carries the argument

The virtual nested scene, a layered imaginary setting that the LLM role-plays within to bypass safety guardrails.

If this is right

  • The same self-losing weakness appears in both open-source models like Llama-2 and Llama-3 and closed-source models like GPT-3.5, GPT-4, and GPT-4o.
  • The induced contents reach higher harmfulness rates than previous jailbreak approaches.
  • Once the nested scene is established, harmful outputs continue in later interactions without repeated prompting.
  • The method requires only ordinary prompting and avoids high-cost computation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Current safety filters may overlook layered fictional contexts because they focus on direct harmful phrasing.
  • Training models to identify and reject instructions that ask for nested role-play could reduce this type of bypass.
  • The approach may transfer to other AI systems that allow creative or persona-based interactions.

Load-bearing premise

Large language models have exploitable personification capabilities that let them build a virtual nested scene to escape safety controls without triggering detection.

What would settle it

Applying the method to a model whose safety training explicitly flags and refuses nested fictional role-play would produce no harmful outputs or lose the effect after the first exchange.

read the original abstract

Large language models (LLMs) have succeeded significantly in various applications but remain susceptible to adversarial jailbreaks that void their safety guardrails. Previous attempts to exploit these vulnerabilities often rely on high-cost computational extrapolations, which may not be practical or efficient. In this paper, inspired by the authority influence demonstrated in the Milgram experiment, we present a lightweight method to take advantage of the LLMs' personification capabilities to construct $\textit{a virtual, nested scene}$, allowing it to realize an adaptive way to escape the usage control in a normal scenario. Empirically, the contents induced by our approach can achieve leading harmfulness rates with previous counterparts and realize a continuous jailbreak in subsequent interactions, which reveals the critical weakness of self-losing on both open-source and closed-source LLMs, $\textit{e.g.}$, Llama-2, Llama-3, GPT-3.5, GPT-4, and GPT-4o. The code and data are available at: https://github.com/tmlr-group/DeepInception.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents DeepInception, a lightweight prompting technique inspired by the Milgram experiment's authority influence. By leveraging LLMs' personification capabilities to build a virtual nested scene, the method aims to bypass safety guardrails adaptively. The authors report that this approach achieves leading harmfulness rates compared to prior methods and enables continuous jailbreaks in follow-up interactions across both open-source (Llama-2, Llama-3) and closed-source (GPT-3.5, GPT-4, GPT-4o) models. Code and data are made available.

Significance. If validated, the work identifies a fundamental 'self-losing' vulnerability in LLM safety alignments and offers an efficient, non-computational alternative to existing jailbreak techniques. The public release of code enhances potential for reproducibility and further research into LLM robustness.

major comments (2)
  1. [Abstract] Abstract: the central claim that the method achieves 'leading harmfulness rates' with previous counterparts cannot be assessed because the abstract (and presumably the results section) provides no quantitative metrics, baseline comparisons, or error analysis.
  2. [Abstract] Abstract (continuous jailbreak paragraph): the claim of realizing a 'continuous jailbreak in subsequent interactions' may be explained by standard conversation-context retention rather than an adaptive, self-sustaining escape from safety mechanisms. Experiments must specify whether follow-up queries occur in independent sessions or within the same thread.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'self-losing' is used without a formal definition; a precise characterization of this weakness would aid interpretation.
  2. [Method] Method description: an explicit example of the nested-scene prompt or pseudocode would improve reproducibility beyond the GitHub link.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and have revised the manuscript to improve clarity and completeness where the feedback identifies gaps.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the method achieves 'leading harmfulness rates' with previous counterparts cannot be assessed because the abstract (and presumably the results section) provides no quantitative metrics, baseline comparisons, or error analysis.

    Authors: We agree that the abstract would be strengthened by including key quantitative results. The full results section reports harmfulness rates across models (Llama-2/3, GPT-3.5/4, GPT-4o) with comparisons to prior methods such as GCG and AutoDAN, along with variability across runs. We have revised the abstract to explicitly state representative harmfulness rates (e.g., over 80% on several models) and direct baseline comparisons. We have also added a brief note on evaluation consistency in the results section to address error analysis. revision: yes

  2. Referee: [Abstract] Abstract (continuous jailbreak paragraph): the claim of realizing a 'continuous jailbreak in subsequent interactions' may be explained by standard conversation-context retention rather than an adaptive, self-sustaining escape from safety mechanisms. Experiments must specify whether follow-up queries occur in independent sessions or within the same thread.

    Authors: We thank the referee for highlighting the need for experimental clarification. Our continuous-jailbreak experiments were performed by issuing follow-up queries within the same conversation thread after the initial DeepInception prompt, allowing the nested virtual scene to sustain the jailbroken state. To address potential confusion with simple context retention, we have added explicit description of the protocol in the revised experimental setup: all reported continuous results use the same thread, while separate ablation tests in fresh sessions confirm the method's effectiveness without relying on prior context. This distinction is now stated in both the abstract and methods. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical prompting method with no derivations or self-referential reductions

full rationale

The paper introduces DeepInception as a lightweight, Milgram-inspired prompting strategy to construct virtual nested scenes that exploit LLM personification for jailbreaking. No equations, parameters, or mathematical derivations appear in the provided text or abstract. The central claim rests on empirical harmfulness rates across Llama-2/3 and GPT models rather than any fitted input renamed as prediction or self-citation chain. The continuous-jailbreak observation is presented as an empirical outcome of retained context in interactions, without reduction to a self-defined quantity. This is a standard empirical contribution with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that LLMs can be reliably induced into nested role-play without guardrail activation, plus the empirical observation that this produces continuous harmful output.

axioms (1)
  • domain assumption LLMs possess personification capabilities that can be leveraged to construct virtual nested scenes bypassing safety controls.
    Invoked when describing the Milgram-inspired method and adaptive escape from usage control.
invented entities (1)
  • Virtual nested scene no independent evidence
    purpose: To enable adaptive jailbreak by framing harmful content inside fictional or authorized inner scenarios.
    New construct introduced in the method; no independent falsifiable evidence outside the prompting experiments is provided.

pith-pipeline@v0.9.0 · 5726 in / 1243 out tokens · 56699 ms · 2026-05-19T10:32:17.692638+00:00 · methodology

discussion (0)

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Forward citations

Cited by 18 Pith papers

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  3. Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory

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  5. SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents

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  6. GRM: Utility-Aware Jailbreak Attacks on Audio LLMs via Gradient-Ratio Masking

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  8. CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

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  9. From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software

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    Curtailing diversity in candidate pools for test-time scaling increases unsafe LLM outputs, as demonstrated by a reference-guided reduction protocol that evades standard safety classifiers across open and closed models.

  11. ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments

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    ReasoningGuard is an inference-time method that uses attention mechanisms to inject safety aha moments and scaling sampling to defend large reasoning models against jailbreak attacks.

  12. Exploring the Secondary Risks of Large Language Models

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  15. ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs

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  17. Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards

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  18. Jailbreak Attacks and Defenses Against Large Language Models: A Survey

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