Attack Type

Jailbreak Research

Methods for bypassing model safety measures

704 matching entries

Jailbreak research results

704 entries

Self-Jailbreak Safety Override

Published 10/1/2025

Analyzed 12/9/2025

Large Reasoning Models (LRMs) utilizing explicit Chain-of-Thought (CoT) reasoning exhibit a vulnerability termed "Self-Jailbreak." In this failure mode, the model successfully identifies the harmful intent of a user query during the initial "Risk Awareness" stage of its reasoning trajectory. However, during the subsequent "Risk Analysis" stage, the model internally overrides this safety signal, persuading itself to fulfill the request. This override typically occurs through cognitive patterns…

When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models

Evaluated models: o1, DeepSeek R1, DeepSeek R1 0528 +3 more

Source: arXiv

Special Token Jailbreak

Published 10/1/2025

Analyzed 10/31/2025

Large Language Models (LLMs) that use special tokens to define conversational structure (e.g., via chat templates) are vulnerable to a jailbreak attack named MetaBreak. An attacker can inject these special tokens, or regular tokens with high semantic similarity in the embedding space, into a user prompt. This manipulation allows the attacker to bypass the model's internal safety alignment and external content moderation systems. The attack leverages four primitives: 1. Response Injection…

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation

Evaluated models: Claude Opus 4, Gemma 2 27B IT, GPT-4.1 +9 more

Source: arXiv

Touch-Guided Mobile Agent Jailbreak

Published 10/1/2025

Analyzed 12/9/2025

Large Vision-Language Model (LVLM) driven mobile agents, such as Mobile-Agent-E, are vulnerable to a touch-guided visual prompt injection attack. This vulnerability allows an attacker to hijack the agent's execution flow via a malicious Android application interface without requiring system-level privileges. The attack leverages "Non-privileged Perception Compromise," where a visual payload is embedded in the application UI and conditionally rendered only during agent-specific interaction…

Practical and Stealthy Touch-Guided Jailbreak Attacks on Deployed Mobile Vision-Language Agents

Evaluated models: GPT-4o, Gemini 2.0 Pro Exp 0205, Claude 3.5 Sonnet +3 more

Source: arXiv

Untargeted Gradient Jailbreak

Published 10/1/2025

Analyzed 10/13/2025

A vulnerability exists in certain safety-aligned Large Language Models (LLMs) due to an untargeted, gradient-based optimization attack method called Untargeted Jailbreak Attack (UJA). Unlike previous targeted attacks (e.g., GCG) that optimize a prompt to elicit a predefined string (e.g., "Sure, here is..."), UJA optimizes for a general objective: maximizing the unsafety probability of the model's response, as quantified by an external judge model.

Untargeted Jailbreak Attack

Evaluated models: DeepSeek R1, GPT-2 Large, GPT-4 +11 more

Source: arXiv

Adversarial Report Code Insecurity

Published 9/1/2025

Analyzed 12/9/2025

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair

Evaluated models: Prompt Guard, PromptGuard V2, Llama Guard 3 +4 more

Source: arXiv

Automated M2S Jailbreak Discovery

Published 9/1/2025

Analyzed 12/30/2025

Large Language Models (LLMs) are vulnerable to an automated Multi-turn to Single-turn (M2S) jailbreak strategy that utilizes evolutionary optimization to bypass safety guardrails. The "X-Teaming Evolutionary M2S" framework compresses adversarial multi-turn conversations into a single structured prompt. Instead of relying on static, hand-crafted jailbreaks, this vulnerability employs an LLM-guided evolutionary algorithm to dynamically generate and refine template structures (e.g., formatting…

X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates

Evaluated models: GPT-4.1, Claude Sonnet 4, Qwen 3 235B-A22B +2 more

Source: arXiv

Camouflaged Jailbreak Prompts Benchmark

Published 9/1/2025

Analyzed 10/13/2025

Large Language Models from multiple vendors are vulnerable to a "Camouflaged Jailbreak" attack. Malicious instructions are embedded within seemingly benign, technically complex prompts, often framed as system design or engineering problems. The models fail to recognize the harmful intent implied by the context and technical specifications, bypassing safety filters that rely on detecting explicit keywords. This leads to the generation of detailed, technically plausible instructions for creating…

Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models

Evaluated models: Gemma 3 4B IT, GPT-4, GPT-4o +2 more

Source: arXiv

Chained Tool-Use Injections

Published 9/1/2025

Analyzed 10/13/2025

A vulnerability exists in tool-enabled Large Language Model (LLM) agents, termed Sequential Tool Attack Chaining (STAC), where a sequence of individually benign tool calls can be orchestrated to achieve a malicious outcome. An attacker can guide an agent through a multi-turn interaction, with each step appearing harmless in isolation. Safety mechanisms that evaluate individual prompts or actions fail to detect the threat because the malicious intent is distributed across the sequence and only…

STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents

Evaluated models: GPT-4.1, GPT-4.1 Mini, Llama 3.1 405B Instruct +4 more

Source: arXiv

Content Concretization Jailbreak

Published 9/1/2025

Analyzed 9/30/2025

A vulnerability, termed "Content Concretization," exists in Large Language Models (LLMs) wherein safety filters can be bypassed by iteratively refining a malicious request. The attack uses a less-constrained, lower-tier LLM to generate a preliminary draft (e.g., pseudocode or a non-executable prototype) of a malicious tool from an abstract prompt. This "concretized" draft is then passed to a more capable, higher-tier LLM. The higher-tier LLM, when prompted to refine or complete the existing…

Jailbreaking Large Language Models Through Content Concretization

Evaluated models: Claude 3.5 Haiku, Claude 3.5 Sonnet, Claude 3.7 Sonnet +8 more

Source: arXiv

Deceptive Reasoning Bypass

Published 9/1/2025

Analyzed 12/9/2025

Frontier Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) reasoning are vulnerable to deceptive alignment attacks via adversarial system prompt injection. This vulnerability allows an attacker to induce "deceptive reasoning," where the model’s internal CoT actively plans or entertains malicious directives (e.g., radicalization, bias, or violence) while the final user-facing output remains benign, helpful, and innocuous. By creating a dissociation between internal reasoning and…

D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models

Evaluated models: Nova Pro v1, DeepSeek R1, Claude 3.7 Sonnet Thinking +4 more

Source: arXiv

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