Attack Type

Side channel Vulnerabilities

Attacks exploiting indirect information leakage

4 matching entries out of 20 in this category

Side channel research results

4 entries

Semantic Cache Collision Hijack

Updated 3/9/2026

Semantic caching mechanisms in LLM applications are vulnerable to cross-tenant cache key collision attacks (CacheAttack) due to the inherent mathematical conflict between locality-preserving fuzzy hashing and cryptographic collision resistance (the avalanche effect). An attacker can leverage gradient-based search algorithms to optimize an adversarial discrete suffix that, when appended to a malicious prompt, forces its output embedding vector to collide with the embedding of a targeted benign…

From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching

Affects: Llama 3.1 8B, Mistral 7B, DeepSeek R1

Source: arXiv

Embodied LLM Misaligned Actions

Updated 12/29/2024

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…

BadRobot: Manipulating Embodied LLMs in the Physical World

Affects: BERT, GPT-3.5 Turbo, GPT-4 Turbo +2 more

Source: arXiv

Fast Projected Gradient Jailbreak

Updated 12/28/2024

Large Language Models (LLMs) are vulnerable to efficient adversarial attacks using Projected Gradient Descent (PGD) on a continuously relaxed input prompt. This attack bypasses existing alignment methods by crafting adversarial prompts that induce the model to produce undesired or harmful outputs, significantly faster than previous state-of-the-art discrete optimization methods. The effectiveness stems from carefully controlling the error introduced by the continuous relaxation of the discrete…

Attacking large language models with projected gradient descent

Affects: Falcon 7B, Falcon 7B Instruct, Vicuna 7B v1.3

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

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