Attack Type

Denial of service Research

Attacks that disrupt model availability

36 matching entries

Denial of service research results

36 entries

Context privilege escalation in AI agent harnesses

Published Sep 1, 2026Analyzed Sep 9, 2026Source: arXiv

Context assembly can promote repository, tool or skill content into higher-priority instructions or persistent state. The paper studies 12 pinned agent-harness versions.

What's in Your Agent's Context? Context Privilege Escalation Attacks against AI Agent Harness

Evaluated models: GPT-5.5, GPT-5.4-mini, Claude Sonnet 4.6+4 more

GPT-5.5
GPT-5.4-mini
Claude Sonnet 4.6
Claude Opus 4.6
Gemini 2.5 Flash
Gemini 2.5 Pro
DeepSeek V4 Flash

Poisoned agent skills amplify coding-agent token use

Published Aug 22, 2026Analyzed Sep 9, 2026Source: arXiv

Third-party skills can inflate coding-agent resource use while an otherwise legitimate task remains functional.

SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents

Evaluated models: GLM-4.7-Flash, GLM-5, GPT-5.4-mini+1 more

GLM-4.7-Flash
GLM-5
GPT-5.4-mini
GPT-5.5

Agent Lifecycle Compound Threats

Published Mar 12, 2026Analyzed Apr 10, 2026Source: arXiv

OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…

Taming openclaw: Security analysis and mitigation of autonomous llm agent threats

Evaluated models: Not reported

Multi-Stream Thinking Collapse

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

A vulnerability in Large Language Models (LLMs) equipped with built-in "thinking" or step-by-step reasoning modes allows attackers to bypass safety alignments, trigger reasoning collapse, and cause resource exhaustion. The vulnerability is exploited via a Multi-Stream Perturbation Attack, which fragments the sequential integrity of a harmful prompt by word-by-word interleaving it with benign auxiliary tasks (e.g., "Explain the water cycle"). By wrapping the benign text streams in specific…

Multi-Stream Perturbation Attack: Breaking Safety Alignment of Thinking LLMs Through Concurrent Task Interference

Evaluated models: Qwen 3 1.7B, Qwen 3 4B, Qwen 3 8B+2 more

Qwen 3 1.7B
Qwen 3 4B
Qwen 3 8B
Qwen 3 Max
Gemini 2.5 Flash

TabooRAG Transferable Blocking

Published Mar 4, 2026Analyzed Mar 8, 2026Source: arXiv

A vulnerability in Retrieval-Augmented Generation (RAG) systems utilizing safety-aligned Large Language Models (LLMs) allows attackers to perform a highly transferable Denial-of-Service (DoS) or "blocking" attack. By injecting a single maliciously crafted document into the RAG knowledge base, attackers can force the LLM to refuse to answer benign queries. Unlike previous attacks that rely on explicit instruction injection or high-perplexity adversarial suffixes—which modern models easily…

When Safety Becomes a Vulnerability: Exploiting LLM Alignment Homogeneity for Transferable Blocking in RAG

Evaluated models: GPT-5.2, GPT-5 Mini, DeepSeek V3.2+4 more

GPT-5.2
GPT-5 Mini
DeepSeek V3.2
Qwen 3 32B
Gemma 3 12B IT
Llama 3 8B Instruct
Ministral 3 8B Instruct-2512

Video-LLM Token Flood

Published Mar 2, 2026Analyzed Mar 9, 2026Source: arXiv

Video-based Large Language Models (Video-LLMs) are vulnerable to a universal Energy-Latency Attack (ELA) that triggers a Denial-of-Service (DoS) via spatially concentrated adversarial patches. Because video architectures rely on temporal subsampling and pooling which act as a low-pass filter against full-frame diffuse noise, an attacker can bypass this compression by anchoring cross-modal attention to a dense, localized visual anomaly. By injecting a fixed, content-agnostic patch into the…

VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models

Evaluated models: LLaVA-NeXT-Video 7B, Qwen3-VL 4B Instruct, Video-LLaVA 7B

Stealthy Tool-Call Token Exhaust

Published Mar 1, 2026Analyzed Mar 8, 2026Source: arXiv

OpenClaw v2026.2.9 is vulnerable to a resource amplification and economic denial-of-service (DoS) attack via malicious third-party skills. An attacker can publish a Trojanized skill that exploits the framework's tool-calling loop and context-management architecture by injecting a multi-turn "Segmented Verification Protocol" (SVP). Malicious instructions embedded in the skill's SKILL.md file mandate extensive autoregressive sequence generation, while a companion script returns PROGRESS, REPAIR…

Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agents

Evaluated models: Gemini 2.5 Pro

Adaptive Web Agent Prompt Injection

Published Feb 9, 2026Analyzed Feb 21, 2026Source: arXiv

Large Language Model (LLM) based web agents (such as those built using the BrowserUse scaffold) are vulnerable to Indirect Prompt Injection (IPI) attacks when autonomously navigating and processing untrusted web content. Unlike standard Cross-Site Scripting (XSS), this vulnerability occurs when the LLM orchestrator consumes the DOM or visual screenshots of a webpage containing concealed or contextually disguised adversarial instructions. The LLM interprets these embedded text strings as…

MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

Evaluated models: GPT-4.1, GPT-4o, Qwen3-VL 32B Instruct

Fill-Squeeze Scheduler DoS

Published Feb 8, 2026Analyzed Feb 21, 2026Source: arXiv

LLM serving frameworks utilizing continuous batching and PagedAttention (such as vLLM, SGLang, and Orca) are vulnerable to a resource exhaustion Denial-of-Service attack known as "Fill and Squeeze." An unprivileged remote attacker can exploit the deterministic state transitions of the scheduler's memory management to induce severe latency or service denial. The attack leverages a side-channel vulnerability where Inter-Token Latency (ITL) correlates linearly with global KV-cache usage due to…

Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model

Evaluated models: Qwen 3 8B, Gemma 3 12B IT, DeepSeek R1 Distill Llama 8B+1 more

Qwen 3 8B
Gemma 3 12B IT
DeepSeek R1 Distill Llama 8B
Llama 3.1 8B Instruct

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

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