Honeypots & Deception Tools 2026
Honeypots and deception technology plant fake assets across your environment, things like decoy servers, dummy credentials, bait files, and canary tokens, that no legitimate user or process should ever touch. The moment something interacts with one, you get a high-fidelity alert with almost no false positives, because there is no benign reason to be there. For security operations teams drowning in noise from EDR and SIEM, deception flips the economics: instead of chasing probabilistic anomalies, you catch attackers who have already bypassed your perimeter and are mapping your network, hunting credentials, or moving laterally. It is a detection layer built on the assumption that prevention sometimes fails.
We cover 216 Honeypots & Deception tools, 193 free and 23 commercial.
Accuracy and depth improve over time. Last reviewed Jun 2026. Is something off? Reach out.
FEATURED
USE CASES
A honeytoken-based tripwire for Microsoft's Active Directory to detect privilege escalation attempts
SMTP honeypot tool with configurable response messages, email storage, and automatic information extraction.
A honeypot tool emulating HL7 / FHIR protocols with various installation and customization options.
A simplified UI for showing honeypot alarms for the DTAG early warning system
A nodejs web application honeypot designed for small environments like Raspberry Pi to capture and analyze malicious web-based attacks.
Hived is a honeypot tool for deceiving attackers and gathering information.
A honeypot that simulates an exposed networked printer using PJL protocol to capture and log attacker interactions through a virtual filesystem.
A low-interaction honeypot that uses Dionaea as its core, providing a simple and easy-to-use interface for setting up and managing honeypots.
A simple Telnet honeypot program that logs login attempts and credentials from botnet attacks, specifically designed to track Mirai botnet activity.
hpfeeds is a lightweight authenticated publish-subscribe protocol with Python 3 compatible broker and client.
A modular web application honeypot framework with automation and logging capabilities.
HoneyFS is an LLM-powered honeypot tool that generates realistic fake file systems using GPT-3.5 to deceive attackers and enhance security analysis.
A high-interaction honeypot solution for detecting and analyzing SMB-based attacks
Honey-Pod for SSH that logs username and password tries during brute-force attacks.
A Go-based honeypot that mimics Intel's AMT management service to detect and log exploitation attempts targeting the CVE-2017-5689 firmware vulnerability.
HoneyThing is a honeypot for Internet of TR-069 things, emulating vulnerabilities and supporting TR-069 protocol.
A DICOM server with a twist, blocking C-STORE attempts for protection but logging them.
A simple honeypot that opens a listening socket and waits for connection attempts, with configurable reply and event handling
ElasticSearch honeypot to capture attempts to exploit CVE-2014-3120, with logging and daemon options.
A web application honeypot sensor attracting malicious traffic from the Internet
A honeypot designed to detect and analyze malicious activities in instant messaging platforms.
Tool for setting up Glutton, a cybersecurity tool for monitoring SSH traffic.
How to choose Honeypots & Deception tools
- Match coverage to your attack surface: endpoint, network, Active Directory, cloud, and OT each demand different decoy types, so confirm the tool covers where your adversaries actually move.
- Judge decoy realism and how lures are seeded. Convincing, automatically distributed bait catches skilled attackers; obvious or stale decoys get fingerprinted and avoided.
- Weigh deployment and maintenance effort. Agentless or automated distribution scales far better than hand-built honeypots, and decoy refresh keeps believability from decaying.
- Inspect forensic depth on engagement. The best tools capture full attacker session detail, tooling, and intent, not just a binary alert that something touched the decoy.
- Verify integration with your SIEM, SOAR, and EDR so deception alerts feed existing response workflows instead of becoming another isolated console.
- Decide between open-source and commercial based on scope: surgical, low-cost coverage versus environment-wide automation, lifecycle management, and support.
Honeypots & Deception Tools FAQ
Common questions about Honeypots & Deception tools, selection guides, pricing, and comparisons.
It is a class of security tools that deploy fake assets, decoy servers, fabricated credentials, bait files, and canary tokens, designed so that any interaction with them signals malicious or unauthorized activity. Because real users never touch these decoys, alerts carry very low false-positive rates. Deception catches attackers during reconnaissance and lateral movement, after they have slipped past preventive controls but before they reach real data.
A classic honeypot is usually a single, isolated decoy system you stand up to study attacker behavior, often deployed and monitored by hand. Modern deception technology scales that idea across the whole environment: it distributes lures and decoys automatically through endpoints, networks, cloud, and Active Directory, then centralizes alerting and forensics. Honeypots are the research primitive; deception platforms operationalize the concept for production detection at enterprise scale.
Begin with what you are protecting and where attackers move: endpoints, AD, cloud, OT, or all of them. Weigh deployment effort and decoy realism, since unconvincing lures get ignored by skilled adversaries. Check how alerts integrate with your SIEM, SOAR, and EDR, what forensic depth you get on engagement, and how the tool handles decoy maintenance so stale bait does not erode believability over time.
Open-source honeypots like canary token generators and low-interaction decoys are excellent for targeted use: monitoring a specific segment, seeding a few high-value lures, or learning the technique cheaply. Commercial deception platforms add automated distribution at scale, decoy lifecycle management, deep forensic capture, and SOC integrations. The split tends to be open-source for surgical coverage, a platform when deception becomes a core, environment-wide detection layer.
It complements them rather than replacing anything. EDR watches real endpoints and SIEM correlates logs, both of which generate volume and require tuning. Deception adds a parallel, low-noise signal: an alert fires only when someone touches something fake, which usually means an intruder is already inside. It is especially strong at catching lateral movement and credential theft that behavioral detection can miss or bury in noise.
