Attack Context

Black-box Research

Attacks requiring no knowledge of model internals

31 matching entries out of 736 in this category

Black-box research results

31 entries

Automated indirect prompt injection against tool-calling agents

Published Jun 9, 2026Analyzed Jul 21, 2026Source: arXiv

The paper presents a concrete, reproducible security evaluation in which attacker-controlled instructions embedded in retrieved external content steer stateful, tool-calling LLM agents toward unauthorized actions. It adapts white-box GCG and black-box TAP to AgentDojo and evaluates single-task and task-universal attacks across 80 task pairs in four domains. The reported results show that semantic black-box optimization can discover functional prompt injections more effectively than…

Assessing Automated Prompt Injection Attacks in Agentic Environments

Evaluated models: Gemma3-4B Instruct, Qwen 3 4B Instruct, GPT-5+6 more

Gemma3-4B Instruct
Qwen 3 4B Instruct
GPT-5
GPT-5 Mini
Qwen 3 32B
Qwen3-235B-A22B (MoE)
Qwen3-235B-A22B-Thinking (MoE)
Claude Sonnet 4.5
Gemini 2.5 Flash

Fluent Single-Document RAG Corpus Poisoning

Published May 27, 2026Analyzed Jul 20, 2026Source: arXiv

SilentRetrieval describes a specific RAG corpus-integrity vulnerability: an attacker able to add a topically relevant document to a retrieval corpus can make that document rank highly and influence the generated answer while remaining fluent enough to evade simple perplexity checks. The paper evaluates a two-stage method combining retrieval-oriented document optimization with context-adaptive claim integration. A safe defensive reproduction is to use only isolated benchmark corpora and inert…

SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning

Evaluated models: Llama 2 7B Chat, Mistral 7B Instruct v0.2, Qwen 7B Chat+1 more

Llama 2 7B Chat
Mistral 7B Instruct v0.2
Qwen 7B Chat
GPT-3.5 Turbo

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

Prompt Length Exponential Jailbreak

Published Mar 11, 2026Analyzed Apr 10, 2026Source: arXiv

A vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to achieve an exponentially scaling Attack Success Rate (ASR) for jailbreaks by combining adversarial prompt injection with repeated inference-time sampling. While ASR against un-injected prompts scales polynomially with the number of generated samples ($k$), introducing a long adversarial suffix acts as a strong "misalignment field." This shifts the model's generation distribution into a replica-symmetric ordered…

Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover

Evaluated models: Claude Sonnet 4.5 20250929, Claude 3.5 Haiku 20241022, GPT 3.5-turbo-0125+7 more

Claude Sonnet 4.5 20250929
Claude 3.5 Haiku 20241022
GPT 3.5-turbo-0125
GPT-4 0613
GPT-4.5 Preview
Llama 3.2 3B Instruct
Llama 3 8B Instruct
Llama 3 70B Instruct
OLMo 2 0325 32B Instruct
OLMo 3.1 32B Instruct

RAG Knowledgebase Exfiltration

Published Feb 10, 2026Analyzed Feb 21, 2026Source: arXiv

Retrieval-Augmented Generation (RAG) systems are vulnerable to iterative knowledge-extraction attacks designed to reconstruct the underlying private knowledge base. The vulnerability exists due to the decoupled optimization of the retrieval and generation phases. Attackers can craft adversarial queries consisting of two distinct components: an "Information" component (optimized via gradient descent or random sampling to steer embeddings toward specific, diverse regions of the vector space) and…

Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation

Evaluated models: GPT-4o, Llama 3 8B, Qwen 2.5 7B

Covert Grade Manipulation

Published Feb 1, 2026Analyzed Feb 22, 2026Source: arXiv

Large Language Models (LLMs) utilized for Automatic Short Answer Grading (ASAG) are vulnerable to the "GradingAttack" framework, which employs fine-grained adversarial manipulation to alter grading outcomes. Attackers can leverage two distinct strategies: (1) Prompt-level attacks using role-play injection strings that instruct the model to pretend an answer is correct regardless of factual accuracy, and (2) Token-level attacks utilizing gradient-based optimization (similar to Greedy Coordinate…

GradingAttack: Attacking Large Language Models Towards Short Answer Grading Ability

Evaluated models: GPT-3.5, GPT-4, GPT-4o+3 more

GPT-3.5
GPT-4
GPT-4o
Llama 3.1 8B
Mistral 7B
Qwen 2.5 7B

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

LLM Router Rerouting

Published Jan 29, 2026Analyzed Mar 8, 2026Source: arXiv

LLM routing systems are vulnerable to adversarial rerouting attacks where malicious triggers prepended to user queries manipulate the router's model-selection mechanism. Because LLM routers function as classifiers evaluating query complexity to balance computational cost and response quality, an attacker can craft adversarial prefixes that distort the query's latent semantic representation. This exploits the router's decision boundaries, forcing the system to misclassify the input and redirect…

RerouteGuard: Understanding and Mitigating Adversarial Risks for LLM Routing

Evaluated models: GPT-4, GPT-4o, GPT-5+2 more

GPT-4
GPT-4o
GPT-5
Llama 3 8B
Mixtral 8x7B

CoT Prefix Jailbreak

Published Jan 7, 2026Analyzed Mar 8, 2026Source: arXiv

A vulnerability exists in Large Language Model (LLM) and Large Reasoning Model (LRM) serving interfaces that allow user-defined response prefixes, such as plain text-completion (v1/completions), Fill-in-the-Middle (FIM), or assistant message prefilling. An attacker can perform a Response Prefix Attack (RPA) by injecting maliciously crafted Chain-of-Thought (CoT) reasoning tokens immediately following the assistant's start delimiter (e.g., <|im_start|>assistant). Because these tokens are placed…

What Matters For Safety Alignment?

Evaluated models: DeepSeek V3.2, Gemini 3 Pro Preview, Gemini 3 Flash Preview+4 more

DeepSeek V3.2
Gemini 3 Pro Preview
Gemini 3 Flash Preview
Grok 4.1 Fast
Claude Sonnet 4.5
GPT-5.2
GPT-4o Mini

TeleAI Reveals Systemic LLM Vulnerabilities

Published Dec 5, 2025Analyzed Mar 8, 2026Source: arXiv

Reasoning-specialized Large Language Models (LLMs) that utilize Chain-of-Thought (CoT) processes are vulnerable to reasoning-exploitation jailbreaks. Attackers can bypass standard safety alignments (such as RLHF) by using adaptive multi-turn interactions or semantic transformations to induce the model to generate intermediate reasoning steps that "rationalize" or "contextualize" a harmful request. Because current alignment techniques often fail to scale linearly with reasoning depth, forcing…

TeleAI-Safety: A comprehensive LLM jailbreaking benchmark towards attacks, defenses, and evaluations

Evaluated models: GPT-5, GPT-4.1, GPT-4.1 Mini+11 more

GPT-5
GPT-4.1
GPT-4.1 Mini
GPT-4o Mini
o1
Grok 3
Grok 3 Mini
Claude 3.5 Sonnet
Gemini 2.5 Pro
Vicuna 7B
Llama 3.1 8B Instruct
DeepSeek R1
Qwen 1.5 7B Chat
Qwen 2.5 7B Instruct

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