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Data security Research

Issues affecting data confidentiality and integrity

17 matching entries out of 171 in this category

Data security research results

17 entries

Long-Horizon Agent Attacks

Published Feb 18, 2026Analyzed Apr 10, 2026Source: arXiv

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…

AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks

Evaluated models: GPT-4o, GPT-5.1, Gemini 3 Flash+1 more

GPT-4o
GPT-5.1
Gemini 3 Flash
Claude Sonnet 4.5

Automated Stealth Skill Injection

Published Feb 15, 2026Analyzed Feb 21, 2026Source: arXiv

A vulnerability exists in LLM-based coding agents that implement modular capability extensions (often referred to as "Agent Skills") where the agent dynamically loads and executes user-provided skill packages. The vulnerability allows for Skill-Based Prompt Injection, specifically leveraging a technique known as "SkillJect." This attack decouples the malicious intent from the operational payload to bypass semantic safety filters. An attacker constructs a skill package containing: 1. Inducement…

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

Evaluated models: Claude Sonnet 4.6, GPT-5 Mini, GLM-4.7+7 more

Claude Sonnet 4.6
GPT-5 Mini
GLM-4.7
MiniMax M2.1
DeepSeek V4 Flash
GLM-5.1
MiniMax M2.7
MiMo V2.7
GPT-5.4
Claude Opus 4.6

Voice Agent Behavioral Bypass

Published Feb 7, 2026Analyzed Feb 21, 2026Source: arXiv

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

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

GPT-4o
GPT-4o Mini
Gemini 1.5 Pro
Gemini 2.5 Flash
Gemini 2.5 Pro
Qwen2-Audio 7B
Qwen 2.5 Omni 7B

CoT Detector Obfuscation Bypass

Published Dec 24, 2025Analyzed Dec 30, 2025Source: arXiv

LLM-based code agents and vulnerability detectors employing Chain-of-Thought (CoT) reasoning are susceptible to automated adversarial code obfuscation. The vulnerability exists because CoT mechanisms expose the model's decision logic, allowing reinforcement learning frameworks (such as CoTDeceptor) to iteratively refine code transformations based on the detector's own reasoning traces. By optimizing for "reasoning instability" and "hallucination" rather than just syntactic evasion, attackers…

CoTDeceptor: Adversarial Code Obfuscation Against CoT-Enhanced LLM Code Agents

Evaluated models: DeepSeek R1, GPT-5

Inter-Agent Computer Takeover

Published Jul 9, 2025Analyzed Dec 30, 2025Source: arXiv

A remote code execution (RCE) and privilege escalation vulnerability exists in Large Language Model (LLM) multi-agent systems and agentic RAG (Retrieval-Augmented Generation) architectures. The vulnerability arises from "Inter-Agent Trust Exploitation," where LLM agents implicitly trust instructions received from peer agents, bypassing safety guardrails and jailbreak defenses that are active during direct human-to-LLM interaction. An attacker can inject a malicious command payload (e.g., a…

The Dark Side of LLMs: Agent-based Attack Vectors for System-level Compromise

Evaluated models: GPT-4o Mini, GPT-4o, GPT-4.1 Mini+15 more

GPT-4o Mini
GPT-4o
GPT-4.1 Mini
GPT-4.1
Claude Sonnet 4
Claude Opus 4
Gemini 2.0 Flash
Gemini 2.5 Flash
Gemini 2.5 Pro
Magistral Medium
Mistral Large
Mistral Small
Llama 3.3 70B
Llama 4 16x17B
Qwen 3 14B
Qwen 3 30B-A3B
Devstral 24B
DeepSeek R1 Tool Calling 70B

Agent Pipeline Simple Hacks

Published Feb 12, 2025Analyzed Dec 9, 2025Source: arXiv

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

Evaluated models: Not reported

Guardrail Bypass Harmful Fine-tuning

Published Jan 29, 2025Analyzed Mar 19, 2025Source: arXiv

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

Evaluated models: Llama 3 8B, Llama Guard 2

LLM Hate Campaign Vulnerability

Published Jan 28, 2025Analyzed Feb 2, 2025Source: arXiv

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

Evaluated models: Baichuan 2, Dolly 2, GPT-3.5 Turbo+2 more

Baichuan 2
Dolly 2
GPT-3.5 Turbo
GPT-4
OPT

BarkPlug Data Poisoning Attack

Published Nov 3, 2024Analyzed Mar 19, 2025Source: arXiv

A poisoning attack against a Retrieval-Augmented Generation (RAG) system that manipulates the retriever component by injecting a poisoned document into the data used by the embedding model. This poisoned document contains modified and incorrect information. When activated, the system retrieves the poisoned document and uses it to generate misleading, biased, and unfaithful responses to user queries.

Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant

Evaluated models: Barkplug V.2

Adversarial Unlearning Bypass

Published Aug 20, 2024Analyzed Jan 26, 2025Source: arXiv

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

Evaluated models: Llama 2 7B Chat, Llama 3 8B Instruct, Llama 3.1 8B Instruct

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