GOODLIAR: A Reinforcement Learning-Based Deceptive Agent for Disrupting LLM Beliefs on Foundational Principles (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances indicate that LLMs exhibit increasingly complex reasoning abilities . |
| Approach: | They propose a reinforcement learning framework that generates deceptive contexts to rewrite an LLM’s core axiomatic beliefs. |
| Outcome: | The proposed framework induces persistent belief shifts rather than one-off policy breaches. |
Similar Papers
RLShield: Dynamic Jailbreak Detection for LLMs via Reinforced Adaptive Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to detect jailbreak prompts rely on static model components or fixed decision thresholds. |
| Approach: | They propose a dynamic jailbreak detection framework that employs reinforcement learning for adaptive threshold selection. |
| Outcome: | Experimental results show that the framework outperforms baselines in detection performance while maintaining high computational efficiency. |
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing research has shown that large language models have difficulty discerning the veracity of their intrinsic answers. |
| Approach: | They propose a jailbreak attack method that generates an aligned language model for malicious output. |
| Outcome: | The proposed method achieves competitive performance with more harmful outputs. |
Diversity Helps Jailbreak Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for jailbreaking large language models rely on laborious human engineering and whitebox access to model internals. |
| Approach: | They propose a method that instructs large language models to deviate from prior context and generate harmful outputs by instructing them to deviat from previous attacks. |
| Outcome: | The proposed method achieves a 62.83% higher success rate in compromising ten leading chatbots, while using only 12.9% of the queries. |
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts . |
| Approach: | They propose a formalism and a taxonomy of known (and possible) jailbreaks . they propose generating a dataset of model outputs across 3700 jailbreak prompts a 'prompt' attack is a new attack popularly categorized as "prompting injection attacks" |
| Outcome: | The proposed model exploits 3700 jailbreak prompts over 4 tasks to analyze their effectiveness . authors show that the model can learn to perform a new task on unseen examples . |
A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency. |
| Approach: | They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts. |
| Outcome: | The proposed framework improves the attack success rate while reducing the time cost compared to baselines. |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
Copied to clipboard
Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
| Approach: | They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. |
| Outcome: | The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM . |
Defending Jailbreak Prompts via In-Context Adversarial Game (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications, but concerns regarding their security persist. |
| Approach: | They propose an adversarial game that leverages agent learning to extend knowledge to defend against jailbreaks. |
| Outcome: | The proposed game shows that LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios. |
Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)
Copied to clipboard
| Challenge: | Commercial large language models (LLMs) have made great progress in various NLP tasks. |
| Approach: | They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing. |
| Outcome: | The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies. |
LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models. |
| Approach: | They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness. |
| Outcome: | The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model. |
from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. |
| Approach: | They propose a framework that exploits AdVersArial meTAphoR to induce LLMs to calibrate harmful metaphors for jailbreaking. |
| Outcome: | The proposed framework can successfully jailbreak Large Language Models (LLMs) by leveraging the AdVersArial meTAphoR (AVATAR) framework achieves state-of-the-art attack success rate across multiple advanced LLMs. |