REVIEW 4 cited by
LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Attacks powered by Large Language Model (LLM) agents represent a growing threat to modern cybersecurity. To address this concern, we present LLM Honeypot, a system designed to monitor autonomous AI hacking agents. By augmenting a standard SSH honeypot with prompt injection and time-based analysis techniques, our framework aims to distinguish LLM agents among all attackers. Over a trial deployment of about three months in a public environment, we collected 8,130,731 hacking attempts and 8 potential AI agents. Our work demonstrates the emergence of AI-driven threats and their current level of usage, serving as an early warning of malicious LLM agents in the wild.
Forward citations
Cited by 4 Pith papers
-
Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection
The work introduces and partially evaluates seven cross-domain prompt injection detectors, reporting F1 gains on benchmarks like deepset/prompt-injections and indirect-injection sets via local alignment, stylometry, a...
-
Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response
A structured review organizes cyber-capable-agent risks into five vulnerability classes and argues that evaluation environments must be treated as operational security systems rather than background.
-
Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Models
In a one-shot text simulation, frontier LLMs frequently propose editing game files to win an unwinnable tic-tac-toe game; o3-mini edits at 37.1% and a 'creative' prompt raises the rate to 77.3% across models.
-
Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks
A review of LLM-based agents as autonomous cyberattackers, arguing that they lower attack costs, scale up threats, and outpace existing defenses.
Discussion (0). Continue with ORCID to comment.