Jingyu Peng, Maolin Wang, Xiangyu Zhao, Kai Zhang, Wanyu Wang, Pengyue Jia, Qidong Liu, Ruocheng Guo, Qi Liu
| Challenge: | Existing attacks on LLM reasoning are constrained by specific settings or lack of imperceptibility, limiting their feasibility and generalizability. |
| Approach: | They propose a stepwise rEasoning error disruption attack that subtly injects errors into prior reasoning steps to mislead the model into producing incorrect subsequent reasoning and final answers. |
| Outcome: | The proposed attack is compatible with zero-shot and few-shot settings, maintains the natural reasoning flow, and ensures covert execution without modification of the instruction. |
Similar Papers
Preemptive Answer “Attacks” on Chain-of-Thought Reasoning (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a scenario where the LLM obtains an answer before engaging in reasoning, and propose two measures to bolster the robustness of this approach. |
| Outcome: | The proposed model significantly impairs its reasoning capability across various CoT methods and a broad spectrum of datasets. |
Critical-CoT: A Robust Defense Framework against Reasoning-Level Backdoor Attacks in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are vulnerable to backdoors that use long-form reasoning to generate a specific word, choice, or class. |
| Approach: | They propose a mechanism that allows LLMs to develop critical thinking behaviors and detect backdoors by a two-stage fine-tuning. |
| Outcome: | The proposed mechanism exhibits strong cross-domain and cross-task generalization. |
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, making them increasingly integral to various applications. |
| Approach: | They establish a benchmark to evaluate the robustness of instruction-following LLMs against prompt injection attacks, assessing their ability to discern which instructions to follow and which to disregard. |
| Outcome: | The proposed model is overly sensitive to prompt injection attacks, focusing on the latter part of the prompt without fully understanding the context. |
LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a high vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Approach: | They propose to use large language models to test their security against jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Outcome: | The proposed model is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories. |
Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent models such as OpenAI o1 and DeepSeek-R1 produce explicit reasoning traces, often via Chain-of-Thought prompting. |
| Approach: | They propose a taxonomy that offers a unified perspective for summarizing existing approaches and categorizing reasoning-based backdoor attacks into associative, passive, and active. |
| Outcome: | The proposed taxonomy categorizes reasoning-based backdoor attacks into associative, passive, and active. |
“Well, Keep Thinking”: Enhancing LLM Reasoning with Adaptive Injection Decoding (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a decoding strategy that nudges LLMs to continue reasoning, thereby preventing immature reasoning processes. |
| Outcome: | The proposed method significantly improves LLM reasoning capabilities on diverse reasoning benchmarks. |
Defense Against Prompt Injection Attack by Leveraging Attack Techniques (2025.acl-long)
Copied to clipboard
| Challenge: | Recent attacks leverage LLMs’ instruction-following abilities and their inabilities to distinguish instructions injected in the data content. |
| Approach: | They invert the intention of prompt injection methods to develop novel defense methods based on previous training-free attack methods by repeating the attack process with the original input instruction rather than the injected instruction. |
| Outcome: | The proposed methods outperform existing defense approaches, achieving state-of-the-art results. |
Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition (2023.emnlp-main)
Copied to clipboard
Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Kost, Christopher Carnahan, Jordan Boyd-Graber
| Challenge: | Large Language Models are increasingly being deployed in interactive contexts that involve direct user engagement. |
| Approach: | They run a global prompt hacking competition to encourage research on prompt hacks . they elicit 600K+ adversarial prompts against three state-of-the-art LLMs based on a dataset . |
| Outcome: | The results of the competition show that current LLMs can be manipulated via prompt hacking . the competition elicits 600K+ adversarial prompts against three state-of-the-art LLM models . |
Vulnerabilities of Large Language Models to Adversarial Attacks (2024.acl-tutorials)
Copied to clipboard
| Challenge: | This tutorial focuses on the vulnerabilities of Large Language Models to adversarial attacks . the tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity . |
| Approach: | This tutorial lays the foundation by explaining safety-aligned LLMs and concepts in cybersecurity. |
| Outcome: | The tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity. |
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) take advantage of step-by-step reasoning instructions . negation is a core linguistic phenomenon that is difficult to process . |
| Approach: | They examine the step-by-step reasoning ability of large language models with a focus on negation . negation is a core linguistic phenomenon that is difficult to process . |
| Outcome: | The proposed models perform better when using chain-of-thought prompting . the results highlight unique limitations in each LLM family . |