Attack Type

Jailbreak Vulnerabilities

Methods for bypassing model safety measures

7 matching entries out of 703 in this category

Jailbreak research results

7 entries

Voice Agent Behavioral Bypass

Updated 2/21/2026

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

Aegis: Towards Governance, Integrity, and Security of AI Voice Agents

Affects: GPT-4o, GPT-4o Mini, Gemini 1.5 Pro +4 more

Source: arXiv

Prompt Injection Alignment Bypass

Updated 12/8/2025

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs

Source: arXiv

Agent Pipeline Simple Hacks

Updated 12/9/2025

Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…

Commercial llm agents are already vulnerable to simple yet dangerous attacks

Source: arXiv

LLM Hate Campaign Vulnerability

Updated 2/2/2025

Large Language Models (LLMs) used in hate speech detection systems are vulnerable to adversarial attacks and model stealing, resulting in evasion of hate speech detection. Adversarial attacks modify hate speech text to evade detection, while model stealing creates surrogate models that mimic the target system's behavior.

HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns

Affects: Baichuan 2, Dolly 2, GPT-3.5 Turbo +2 more

Source: arXiv

Adversarial Unlearning Bypass

Updated 1/26/2025

Large Language Models (LLMs) employing gradient-ascent based unlearning methods are vulnerable to a dynamic unlearning attack (DUA). DUA leverages optimized adversarial suffixes appended to prompts, reintroducing unlearned knowledge even without access to the unlearned model's parameters. This allows an attacker to recover sensitive information previously designated for removal.

Towards robust knowledge unlearning: An adversarial framework for assessing and improving unlearning robustness in large language models

Affects: Llama 2 7B Chat, Llama 3 8B Instruct, Llama 3.1 8B Instruct

Source: arXiv

LLM Causal Neuron Attack

Updated 12/28/2024

Large Language Models (LLMs) such as Llama 2 and Vicuna exhibit a vulnerability where specific layers (e.g., layer 3 in Llama2-13B, layer 1 in Llama2-7B and Vicuna-13B) overfit to harmful prompts, resulting in a disproportionate influence on the model's output for such prompts. This overfitting creates a narrow "safety" mechanism easily bypassed by adversarial prompts designed to avoid triggering these specific layers. Additionally, a single neuron (e.g., neuron 2100 in Llama2 and Vicuna)…

Causality analysis for evaluating the security of large language models

Affects: GPT-3.5 Turbo, GPT-NeoX, Llama 2-13B-chat-hf +2 more

Source: arXiv

Parametric Model Unalignment

Updated 12/28/2024

Large Language Models (LLMs) employing Reinforcement Learning from Human Feedback (RLHF) and instruction tuning methods may exhibit superficial safety guardrails vulnerable to parametric red-teaming attacks. Fine-tuning the model on a dataset of harmful prompts and their corresponding helpful (but harmful) responses can bypass built-in safety mechanisms, resulting in the model generating unsafe outputs. This vulnerability is demonstrated by achieving an 88% success rate in eliciting harmful…

Language model unalignment: Parametric red-teaming to expose hidden harms and biases

Affects: Claude 1, Claude 2, GPT-4 +6 more

Source: arXiv

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