Impact

Data security Vulnerabilities

Issues affecting data confidentiality and integrity

3 matching entries out of 153 in this category

Data security research results

3 entries

Aggressive Compression Model Theft

Updated 2/21/2026

Large Language Models (LLMs) hosted on inference servers are vulnerable to high-speed weight exfiltration attacks due to the inherent compressibility of transformer parameters when decompression constraints are relaxed. Adversaries with compromised server access can utilize aggressive lossy compression techniques—specifically additive quantization combined with k-means clustering—to reduce model size by factors of 16x to 100x (e.g., <1 bit per parameter). Unlike standard quantization for…

Aggressive Compression Enables LLM Weight Theft

Affects: Qwen 2 1.5B, Qwen 2 7B, Qwen 2.5 0.5B +2 more

Source: arXiv

Guardrail Bypass Harmful Fine-tuning

Updated 3/19/2025

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation

Affects: Llama 3 8B, Llama Guard 2

Source: arXiv

GCG Suffix Data Exfiltration

Updated 3/24/2025

A Cross-Prompt Injection Attack (XPIA) can be amplified by appending a Greedy Coordinate Gradient (GCG) suffix to the malicious injection. This increases the likelihood that a Large Language Model (LLM) will execute the injected instruction, even in the presence of a user's primary instruction, leading to data exfiltration. The success rate of the attack depends on the LLM's complexity; medium-complexity models show increased vulnerability.

WHITE PAPER: A Brief Exploration of Data Exfiltration using GCG Suffixes

Affects: GPT-3.5 Turbo, GPT-4o, Phi 3 Mini

Source: arXiv

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