🍯 Discover AI-driven deception resources, including honeypots, datasets, and research, to enhance your defense against cyber threats and adversarial attacks.

AI LLM Firewall -- Detection, Deception, and Intelligence for LLM Security. 9 SDK integrations, 12 LLM backends, 0% false positives. Apache 2.0.

Koney is a Kubernetes operator that enables you to define so-called deception policies for your cluster. Koney automates the setup, rotation, and teardown of honeytokens and fake API endpoints, and uses eBPF to detect, log, and forward alerts when your traps have been accessed.

An AI-driven adaptive honeypot framework that dynamically deceives attackers, analyzes behavior using machine learning and reinforcement learning, and generates actionable threat intelligence mapped to the MITRE ATT&CK framework.

An AI-driven adaptive honeypot framework that dynamically deceives attackers, analyzes behavior using machine learning and reinforcement learning, and generates actionable threat intelligence mapped to the MITRE ATT&CK framework.

FLUIDOS Cyber Deception service

This prototype hooks into the send and receive functions of glibc to insert deceptive elements into HTTP packets.

An awesome list of resources on AI cyber deception, exploring adversarial machine learning techniques used to deceive and secure systems

This project is about how honeypots can be misused to potentially perform DDoS attacks. It has the code to reproduce our work for the DTU course 02334-Research Topics For CyberSecurity.

An awesome list of resources on deception-based security with honeypots and honeytokens

An updated compilation of tools, frameworks, references, and papers relating to cognition-based defenses.

Junk credential generator for SQL in CSV format, intended to introduce uncertainty to attacker exfiltrated data.

MITRE Engage™ is a framework for conducting Denial, Deception, and Adversary Engagements.

Controllable Fake Document Infilling for Cyber Deception (Findings of EMNLP 2022)