Attack Type

Denial of service Vulnerabilities

Attacks that disrupt model availability

6 matching entries out of 34 in this category

Denial of service research results

6 entries

Agent Lifecycle Compound Threats

Updated 4/10/2026

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

Source: arXiv

TabooRAG Transferable Blocking

Updated 3/8/2026

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

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

Source: arXiv

Invisible Prompt Phishing Evasion

Updated 3/8/2026

Multimodal LLM-based phishing detection systems are vulnerable to indirect prompt injection via "perceptual asymmetry." Attackers can embed hidden instructions within a phishing site's HTML, CSS, URLs, or rendered images that remain imperceptible to human victims but are parsed and executed by the evaluating LLM. This vulnerability allows threat actors to manipulate the LLM's contextual understanding, forcing it to misclassify malicious sites as benign (Legitimate Pretexting), trigger safety…

Clouding the Mirror: Stealthy Prompt Injection Attacks Targeting LLM-based Phishing Detection

Affects: GPT-5, Grok 4 Fast Non-Reasoning, Llama 4 Maverick +1 more

Source: arXiv

Adversarial Report Code Insecurity

Updated 12/9/2025

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair

Affects: Prompt Guard, PromptGuard V2, Llama Guard 3 +4 more

Source: arXiv

LLM RAG Decoy Overthink

Updated 12/9/2025

A resource exhaustion and algorithmic complexity vulnerability exists in applications utilizing Reasoning Large Language Models (e.g., OpenAI o1, DeepSeek R1) that process untrusted external context (such as Retrieval-Augmented Generation systems). The vulnerability, dubbed "OverThink," allows an attacker to perform an indirect prompt injection by embedding "decoy" reasoning problems—specifically computation-intensive tasks like Sudoku puzzles or Markov Decision Processes (MDPs)—into the…

Overthink: Slowdown attacks on reasoning llms

Affects: o1, o3, DeepSeek R1

Source: arXiv

Adversarial Tool Injection Attacks

Updated 12/29/2024

Large Language Model (LLM) tool-calling systems are vulnerable to adversarial tool injection attacks. Attackers can inject malicious tools ("Manipulator Tools") into the tool platform, manipulating the LLM's tool selection and execution process. This allows for privacy theft (extracting user queries), denial-of-service (DoS) attacks against legitimate tools, and unscheduled tool-calling (forcing the use of attacker-specified tools regardless of relevance). The attack exploits vulnerabilities…

From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection

Affects: GPT-4o Mini, Llama 3 8B Instruct, Qwen 2 7B Instruct

Source: arXiv

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