arXiv:2602. 09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf.
arXiv:2606. 13385v1 Announce Type: cross Abstract: Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content and execute actions with direct consequences.
The paper examines how AI agents that interact directly with web service backends alter the exploitation of existing web vulnerabilities. Using the VELA benchmark, which includes 44 vulnerabilities across twelve application families, the authors evaluate three commercial agents and find that exploitability is largely determined by the interface design rather than the agent itself, with 91% of vulnerabilities remaining exploitable under the reference configuration. Interface restrictions can reduce attack success, yet the agents generally do not introduce new failures, and in some cases even complete attacks that users omitted.
arXiv:2606. 02240v1 Announce Type: cross Abstract: Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, Salesforce, or Jira accessed through tool calls) whose response content the user neither writes nor controls.
arXiv:2606.00497v2 Announce Type: replace-cross Abstract: Deceptive web content, widely instantiated across the internet and commonly known as \textit{social-engineering attacks}, manipulates autonom...
arXiv:2608.30207v1 Announce Type: cross Abstract: Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal...
AutoRedClaw is an automated red‑teaming framework designed for claw‑style agents such as OpenClaw, which maintain persistent configuration, memory, and reusable skills. It generates end‑to‑end attacks and the necessary environments, using a strict quality‑checking protocol and execution‑based validation to ensure realistic, effective samples. The framework produces a 330‑case benchmark, RedClaw‑Bench, achieving over 30% effective attack rate at less than $0.10 per successful sample and demonstrating higher attack success rates than existing benchmarks across multiple agent systems.
arXiv:2512. 23128v2 Announce Type: replace-cross Abstract: Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking.
arXiv:2608. 15108v1 Announce Type: cross Abstract: Large language model agents are increasingly connected to high-value resources such as computing infrastructure, credentials, usage budgets, identities, private knowledge, communication channels, and organizational workflows.
arXiv:2511. 20597v2 Announce Type: replace-cross Abstract: The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application threat models.
arXiv:2610.02869v1 Announce Type: cross Abstract: Autonomous penetration testing agents conduct multi-step attacks by continuously adapting their plans and actions to target responses. As a common de...
arXiv:2610.09240v1 Announce Type: cross Abstract: Modern web agents built on large vision-language models process webpages, select relevant UI elements, and translate model outputs into browser actio...