Attack Type

Jailbreak Vulnerabilities

Methods for bypassing model safety measures

204 matching entries out of 703 in this category

Jailbreak research results

204 entries

Agent Implicit Doc Execution

Updated 4/10/2026

LLM-based coding agents are vulnerable to Document-Driven Implicit Payload Execution (DDIPE) via supply-chain poisoning of third-party agent skills. Attackers can embed malicious logic directly into legitimate-looking code examples and configuration templates within skill documentation files (e.g., SKILL.md). Because coding agents ingest this metadata into their context windows and treat the documentation as an authoritative reference, the underlying LLM silently reproduces and executes the…

Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems

Affects: Claude Sonnet 4.6, GLM-4.7, MiniMax M2.5 +2 more

Source: arXiv

Agentic Robot Instruction Attack

Updated 4/10/2026

Vision-Language-Action (VLA) models are vulnerable to targeted, low-budget textual perturbations in their natural-language instruction inputs, which can maliciously alter sequential decision-making and downstream physical robotic behavior. Because VLA policies tightly couple language, perception, and control, bounded edits—such as character-level typos, token attribute swaps, or prompt-level uncertainty clauses—propagate through the model's execution trajectory. This allows a black-box…

SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models

Source: arXiv

Arbitrary Agent Topology Breach

Updated 4/10/2026

A vulnerability in LLM-based Multi-Agent Systems (LLM-MAS) allows an attacker who controls a single arbitrary agent to map and extract the system's entire confidential communication topology. Unlike prior attacks that rely on direct identity queries and administrative privileges, this attack infers topology stealthily purely from contextual and linguistic signals (stylometry, role-specific syntax), bypassing standard keyword-based and identity-filtering defenses. The exploit relies on a…

WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference

Affects: Llama 3 70B, Llama 3.1 8B, Mistral Large 12B +3 more

Source: arXiv

Autoresearch Adversarial Algorithm Discovery

Updated 4/11/2026

GPT-OSS-Safeguard-20B and Meta-SecAlign (70B/8B) are vulnerable to white-box adversarial attacks generated by automated algorithmic recombination (specifically the claude_v63, claude_v82, and claude_v53-oss optimizers). These algorithms significantly outperform standard discrete optimization methods (like GCG) by integrating continuous optimization (ADC) with LayerNorm gradient scaling (LSGM), or by merging momentum-smoothed gradients with directional perturbation candidate selection (DPTO)…

Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

Affects: Llama 2 7B, Llama 3 8B, Qwen 2.5 7B +2 more

Source: arXiv

Contextual Priority Hacking

Updated 4/10/2026

Large Language Models (LLMs) are vulnerable to a jailbreak technique termed "Priority Hacking." Adversaries can bypass safety alignments by exploiting the model's internal priority graph, where certain abstract values (e.g., justice, public health) implicitly outweigh general safety restrictions within specific contexts. By crafting a deceptive prompt that frames a malicious request as a necessary action in service of a higher-priority benign value, attackers engineer a value conflict. The…

Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph

Source: arXiv

Inaudible Ultrasonic LLM Jailbreak

Updated 4/10/2026

Speech-driven Large Language Models (LLMs) and end-to-end Large Audio-Language Models (LALMs) are vulnerable to inaudible near-ultrasonic prompt injections, a framework dubbed Sirens' Whisper (SWhisper). By exploiting the non-linear response of commodity microphones, attackers can encode structured, phonetically optimized adversarial prompts into the 17–22 kHz near-ultrasonic band. Using regularized channel-inversion pre-compensation, the attacker shapes the waveform to account for microphone…

Sirens' Whisper: Inaudible Near-Ultrasonic Jailbreaks of Speech-Driven LLMs

Affects: GLM-4 Voice, Qwen Omni Turbo, Llama 3.1 8B Instruct +5 more

Source: arXiv

LLM Judge Manipulation

Updated 4/10/2026

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

Security in LLM-as-a-Judge: A Comprehensive SoK

Affects: GPT-4o, o1, Qwen 2.5 72B Instruct +1 more

Source: arXiv

Prompt Length Exponential Jailbreak

Updated 4/10/2026

A vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to achieve an exponentially scaling Attack Success Rate (ASR) for jailbreaks by combining adversarial prompt injection with repeated inference-time sampling. While ASR against un-injected prompts scales polynomially with the number of generated samples ($k$), introducing a long adversarial suffix acts as a strong "misalignment field." This shifts the model's generation distribution into a replica-symmetric ordered…

Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover

Affects: Claude Sonnet 4.5 20250929, Claude 3.5 Haiku 20241022, GPT 3.5-turbo-0125 +7 more

Source: arXiv

Social Bot Exposure Jailbreak

Updated 3/8/2026

LLM-powered automated social media accounts (bots) are vulnerable to prompt injection via public user replies. When an automated bot scrapes and processes social media engagement to generate responses, an attacker can submit an instruction-override command within a direct reply. Because the underlying LLM fails to isolate its core system instructions (e.g., maintaining a specific political persona) from untrusted user input, the injected command hijacks the model's context window. This forces…

Ignore All Previous Instructions: Jailbreaking as a de-escalatory peace building practise to resist LLM social media bots

Source: arXiv

Activation Delta Detector Evasion

Updated 3/9/2026

Activation-delta-based linear probes used for detecting task drift and prompt injections in Large Language Models (LLMs) can be bypassed using universal adversarial suffixes. By utilizing the Greedy Coordinate Gradient (GCG) algorithm, an attacker can generate a single, optimized suffix that simultaneously fools multiple logistic regression classifiers attached to different hidden layers of the LLM. The attack jointly optimizes the suffix tokens by accumulating gradients from the losses of all…

Bypassing Prompt Injection Detectors through Evasive Injections

Affects: Llama 3 8B, Phi-3 8B

Source: arXiv

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