Attack Type

Side channel Research

Attacks exploiting indirect information leakage

3 matching entries out of 21 in this category

Side channel research results

3 entries

Logit Leakage Model Clone

Published Aug 31, 2025Analyzed Dec 9, 2025Source: arXiv

Large Language Model (LLM) inference APIs that expose top-k logits or log-probabilities are vulnerable to model extraction and cloning. An attacker can execute a two-stage attack to replicate the proprietary model without access to weights, gradients, or training data. First, by submitting fewer than 10,000 random queries and aggregating the returned unrounded logits, the attacker recovers the model's output projection matrix using Singular Value Decomposition (SVD). Second, the attacker…

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation

Evaluated models: GPT-3.5, Mistral 7B

LLM Censorship Vector Control

Published Apr 23, 2025Analyzed May 4, 2025Source: arXiv

Large Language Models (LLMs) employing safety mechanisms based on supervised fine-tuning and preference alignment exhibit a vulnerability to "steering" attacks. Maliciously crafted prompts or input manipulations can exploit representation vectors within the model to either bypass censorship ("refusal-compliance vector") or suppress the model's reasoning process ("thought suppression vector"), resulting in the generation of unintended or harmful outputs. This vulnerability is demonstrated…

Steering the CensorShip: Uncovering Representation Vectors for LLM" Thought" Control

Evaluated models: DeepSeek R1 Distill Qwen 1.5B, DeepSeek R1 Distill Qwen 32B, DeepSeek R1 Distill Qwen 7B+8 more

DeepSeek R1 Distill Qwen 1.5B
DeepSeek R1 Distill Qwen 32B
DeepSeek R1 Distill Qwen 7B
Gemma 2B
Gemma 7B
Llama 2 7B
Llama 3.1 8B
Qwen 1.8B
Qwen 2.5 7B
Qwen 7B
Yi 1.5 6B

LLM Causal Neuron Attack

Published Dec 13, 2023Analyzed Dec 28, 2024Source: arXiv

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

Evaluated models: GPT-3.5 Turbo, Llama 2-13B-chat-hf, Llama 2-7B-chat-hf+1 more

GPT-3.5 Turbo
Llama 2-13B-chat-hf
Llama 2-7B-chat-hf
Vicuna-13B Version 1.5

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