Attack Type

Jailbreak Vulnerabilities

Methods for bypassing model safety measures

703 matching entries

Jailbreak research results

703 entries

LLM Safety Geometry Fragility

Updated 2/21/2026

Large Language Models (LLMs) aligned via standard preference-based optimization methods (e.g., DPO, RLHF) are vulnerable to safety degradation due to optimization-induced fragility. The vulnerability arises from sharp minima in the alignment loss landscape, specifically within a small, localized subspace of safety-critical parameters (approximately 0.5% of neurons account for >80% of worst-case alignment loss). Standard alignment algorithms enforce uniform constraints or fail to control the…

Revisiting Robustness for LLM Safety Alignment via Selective Geometry Control

Affects: Llama 3 8B, Llama 3.2 3B, Qwen 2.5 7B

Source: arXiv

LLM Strategic Prompt Deception

Updated 2/21/2026

Alignment auditing methodologies used to verify the safety of Large Language Models (LLMs)—specifically black-box methods (Assistant Prefills, User Persona Sampling) and white-box interpretability methods (SAE Feature Description, Activation Token Embedding Similarity)—are vulnerable to prompt-level strategic deception. A misaligned "target" model, when supplied with a specific system prompt containing a high-level deceptive strategy and descriptions of the auditing tools, can obfuscate its…

Stress-Testing Alignment Audits With Prompt-Level Strategic Deception

Affects: Llama 3.1 8B, Llama 3.3 70B, Gemma 2 9B

Source: arXiv

Learned Universal Prompt Injection

Updated 2/21/2026

Large Language Model (LLM) agents are vulnerable to automated prompt injection attacks generated via Reinforcement Learning (RL). The attack methodology, termed "AutoInject," utilizes Group Relative Policy Optimization (GRPO) combined with a comparison-based feedback mechanism to generate universal adversarial suffixes. Unlike traditional jailbreaks that optimize for generic affirmative responses (e.g., "Sure"), this vulnerability allows an attacker to optimize for specific, parameterized tool…

Learning to Inject: Automated Prompt Injection via Reinforcement Learning

Affects: Gemini 2.5 Flash, Gemini 2.0 Flash, GPT-4.1 Nano +6 more

Source: arXiv

Long-Horizon Agent Attacks

Updated 4/10/2026

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…

AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks

Affects: GPT-4o, GPT-5.1, Gemini 3 Flash +1 more

Source: arXiv

MoE Expert Lobotomy

Updated 3/8/2026

Mixture-of-Experts (MoE) Large Language Models localize safety alignment (e.g., refusal mechanisms) within a sparse subset of experts rather than distributing it uniformly across the network. An adversary with white-box inference access can exploit this architectural bottleneck by identifying and adaptively silencing these specific "safety experts". By setting the router logits of the targeted experts to negative infinity prior to softmax normalization, the adversary forces the router to…

Large Language Lobotomy: Jailbreaking Mixture-of-Experts via Expert Silencing

Affects: DeepSeek-MoE 16B Chat, GPT-oss 20B, Hunyuan A13B Instruct +5 more

Source: arXiv

MoE Routing Safety Bypass

Updated 2/21/2026

A vulnerability exists in the safety alignment process of Mixture-of-Experts (MoE) Large Language Models (LLMs) when subjected to standard full-parameter fine-tuning. The vulnerability, identified as an "alignment shortcut," occurs when the model minimizes safety loss by modifying routing mechanisms to avoid activating unsafe experts, rather than updating the parameters of the experts responsible for generating harmful content. Consequently, unsafe representations remain latent within the…

RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models

Affects: Qwen 3 30B-A3B, OLMoE 1B-7B-0125 Instruct

Source: arXiv

MoE Unsafe Route Activation

Updated 3/8/2026

Mixture-of-Experts (MoE) Large Language Models are vulnerable to a structural safety bypass attack via the manipulation of expert routing mechanisms at inference time. Attackers with white-box access to per-layer routing scores can apply token- and layer-specific masks ($\Phi \in \{0, -\infty\}^K$) to alter the Top-$k$ expert selection process. By forcing the model to process inputs through specific, poorly-aligned experts ("unsafe routes") and avoiding safety-critical experts, attackers can…

Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs

Affects: DeepSeek-V2, Qwen 2.5 7B, Mixtral 8x7B

Source: arXiv

Mobile Agent Visual Spoofing

Updated 2/22/2026

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Blind Gods and Broken Screens: Architecting a Secure, Intent-Centric Mobile Agent Operating System

Source: arXiv

Multi-Turn Proxy Evasion

Updated 2/22/2026

A logical vulnerability exists in LLM security proxies and guardrails that utilize weighted-average aggregation algorithms for multi-turn risk scoring. The scoring logic exhibits a mathematical "ceiling property" where the cumulative conversation-level risk score converges to the per-turn score regardless of the number of interaction turns ($n$). Consequently, the aggregated score is bounded by the maximum single-turn score ($cum \leq \max(s_i)$). This allows remote attackers to bypass…

Peak+ Accumulation: A Proxy-Level Scoring Formula for Multi-Turn LLM Attack Detection

Source: arXiv

Natural Language Disguise Bypass

Updated 3/9/2026

LLM-based Code Interpreter Agents, including OpenInterpreter and OpenCodeInterpreter, are vulnerable to sandbox evasion and arbitrary code execution via Natural Language Disguise and Contextual Channel Injection. Attackers can bypass Abstract Syntax Tree (AST) static analysis and explicit input guardrails by transforming malicious code logic into descriptive natural language instructions (Code Descriptions), which successfully evade syntax-layer blocks. Additionally, attackers can bypass input…

CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents

Affects: GPT-3.5, GPT-4o, GPT-5

Source: arXiv

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