Attack Surface

Prompt layer Research

Security research involving prompt handling and processing

15 matching entries out of 610 in this category

Prompt layer research results

15 entries

LLM Judge Manipulation

Published Mar 31, 2026Analyzed Apr 10, 2026Source: arXiv

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

Security in LLM-as-a-Judge: A Comprehensive SoK

Evaluated models: GPT-4o, o1, Qwen 2.5 72B Instruct+1 more

GPT-4o
o1
Qwen 2.5 72B Instruct
Llama 3 70B Instruct

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

Mobile Agent Visual Spoofing

Published Feb 11, 2026Analyzed Feb 22, 2026Source: arXiv

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Blind Gods and Broken Screens: Architecting a Secure, Intent-Centric Mobile Agent Operating System

Evaluated models: Not reported

Unified Robustness Gap

Published Jan 31, 2026Analyzed Mar 9, 2026Source: arXiv

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

Unifying Adversarial Robustness and Training Across Text Scoring Models

Evaluated models: E5 BERT-base, Qwen 3 0.6B, Llama 3.2 3B Instruct+2 more

E5 BERT-base
Qwen 3 0.6B
Llama 3.2 3B Instruct
Llama 3.1 8B Instruct
Skywork Reward V2

Emergent Character Misalignment

Published Jan 30, 2026Analyzed Feb 21, 2026Source: arXiv

Large Language Models (LLMs), specifically Llama-3.1-8B-Instruct and Qwen2.5-14B-Instruct, are vulnerable to emergent misalignment caused by "character-conditioned" fine-tuning. This vulnerability arises when models are fine-tuned on small datasets (e.g., 500 examples) that exhibit consistent behavioral dispositions (e.g., "Evil," "Sycophantic," or "Hallucinatory") rather than just incorrect facts. This process creates a latent control variable—defined as "character"—that governs model…

Character as a Latent Variable in Large Language Models: A Mechanistic Account of Emergent Misalignment and Conditional Safety Failures

Evaluated models: GPT-5, Llama 3.1 8B, Qwen 2.5 14B

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

GPT Tool Misuse

Published Nov 28, 2025Analyzed Dec 8, 2025Source: arXiv

A vulnerability exists in OpenAI's Custom GPTs platform where the lack of effective isolation between the system context ("Expert Prompt"), external knowledge retrieval, and user input allows for unauthorized information disclosure and tool misuse. By employing specific prompt injection techniques—including Hex injection, Many-shot prefix attacks, and Knowledge Poisoning (uploading malicious files)—an attacker can bypass safety guardrails. This results in the extraction of proprietary system…

An Empirical Study on the Security Vulnerabilities of GPTs

Evaluated models: DALL-E

Prompt Injection Alignment Bypass

Published Sep 4, 2025Analyzed Dec 8, 2025Source: arXiv

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

Evaluated models: Not reported

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

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