Cover Image

LLM Agents can Autonomously Exploit One-day Vulnerabilities

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fang, Richard, Bindu, Rohan, Gupta, Akul, Kang, Daniel
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LLM Agents can Autonomously Hack Websites
by: Fang, Richard, et al.
Published: (2024)

Teams of LLM Agents can Exploit Zero-Day Vulnerabilities
by: Zhu, Yuxuan, et al.
Published: (2024)

AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
by: Henke, Julius
Published: (2025)

CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
by: Zhu, Yuxuan, et al.
Published: (2025)

Atomicity for Agents: Exposing, Exploiting, and Mitigating TOCTOU Vulnerabilities in Browser-Use Agents
by: Jiang, Linxi, et al.
Published: (2026)

Agent-Sentry: Bounding LLM Agents via Execution Provenance
by: Sequeira, Rohan, et al.
Published: (2026)

ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents
by: Lee, Seunghyun, et al.
Published: (2026)

VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection
by: Nie, Yuzhou, et al.
Published: (2025)

Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities
by: Mouzouni, Charafeddine
Published: (2026)

Life-Cycle Routing Vulnerabilities of LLM Router
by: Lin, Qiqi, et al.
Published: (2025)

From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows
by: Ferrag, Mohamed Amine, et al.
Published: (2025)

Autonomous LLM Agents & CTFs: A Second Look
by: Bouchari, Youness, et al.
Published: (2026)

ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?
by: Wang, Zhun, et al.
Published: (2026)

"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models
by: Tan, Zhen, et al.
Published: (2024)

Amplified Vulnerabilities: Structured Jailbreak Attacks on LLM-based Multi-Agent Debate
by: Qi, Senmao, et al.
Published: (2025)

ASPI: Seeking Ambiguity Clarification Amplifies Prompt Injection Vulnerability in LLM Agents
by: Sehwag, Udari Madhushani, et al.
Published: (2026)

Mind the Gap: Time-of-Check to Time-of-Use Vulnerabilities in LLM-Enabled Agents
by: Lilienthal, Derek, et al.
Published: (2025)

CompressionAttack: Exploiting Prompt Compression as a New Attack Surface in LLM-Powered Agents
by: Liu, Zesen, et al.
Published: (2025)

ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense
by: Lau, Nancy, et al.
Published: (2026)

AI Agent Smart Contract Exploit Generation
by: Gervais, Arthur, et al.
Published: (2025)

Hallucination as Exploit: Evidence-Carrying Multimodal Agents
by: Zhang, Guijia, et al.
Published: (2026)

Enhancing LLM-based Autonomous Driving Agents to Mitigate Perception Attacks
by: Song, Ruoyu, et al.
Published: (2024)

Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
by: Gasmi, Tarek, et al.
Published: (2025)

The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents
by: Ding, Xuwei, et al.
Published: (2026)

Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
by: Deng, Xinhao, et al.
Published: (2026)

The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities
by: Litvak, Ron
Published: (2026)

ARACNE: An LLM-Based Autonomous Shell Pentesting Agent
by: Nieponice, Tomas, et al.
Published: (2025)

Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems
by: Rathnasuriya, Ravishka, et al.
Published: (2025)

LProtector: An LLM-driven Vulnerability Detection System
by: Sheng, Ze, et al.
Published: (2024)

AttackPilot: Autonomous Inference Attacks Against ML Services With LLM-Based Agents
by: Wu, Yixin, et al.
Published: (2025)

Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents
by: Puppala, Sai, et al.
Published: (2026)

Exploiting LLM Quantization
by: Egashira, Kazuki, et al.
Published: (2024)

Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agents
by: Maloyan, Narek, et al.
Published: (2026)

Prompt-in-Content Attacks: Exploiting Uploaded Inputs to Hijack LLM Behavior
by: Lian, Zhuotao, et al.
Published: (2025)

APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks
by: ElZemity, Adel, et al.
Published: (2026)

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
by: Foroughi, Mohammad Hadi, et al.
Published: (2026)

Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence
by: Meng, Yuqiao, et al.
Published: (2025)

LLM-based Vulnerable Code Augmentation: Generate or Refactor?
by: Ouchebara, Dyna Soumhane, et al.
Published: (2025)

SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection
by: Hasan, Touseef, et al.
Published: (2026)

Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations
by: Collu, Matteo Gioele, et al.
Published: (2026)