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.
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USE CASES
A WordPress plugin that logs failed login attempts to help monitor unauthorized access attempts on WordPress websites.
A honeypot system that allows you to set up a decoy API to detect and analyze potential security threats.
An SDN honeypot tool for detecting and analyzing malicious activities in Software-Defined Networking environments.
A honeypot tool to detect and log CVE-2019-19781 scan and exploitation attempts.
A low-interaction SSH authentication logging honeypot that logs all authentication attempts in JSON format.
Create deceptive webpages to deceive and redirect attackers away from real websites by cloning them.
A web-based visualization tool that displays statistics and generates charts from Shockpot honeypot data stored in PostgreSQL databases.
Honeypot tool with bug-catching capabilities and support for multiple protocols.
A low-interaction honeypot to detect and analyze attempts to exploit the CVE-2017-10271 vulnerability in Oracle WebLogic Server
A low interaction honeypot to detect CVE-2018-2636 in Oracle Hospitality Applications.
A plugin repository that extends the Honeycomb honeypot framework with additional features and capabilities for enhanced threat detection and analysis.
An Apache 2 based honeypot with detection capabilities specifically designed to identify and analyze Struts CVE-2017-5638 exploitation attempts.
Open-source honeypot tool for detecting and analyzing malicious activities in the Apache Struts exploit.
Medium interaction SSH honeypot for logging brute force attacks and shell interactions.
A low interaction Python honeypot designed to mimic various services and ports to attract attackers and log access attempts.
A PHP port of Rack::Honeypot, a spam trap that detects and blocks spambots
A combination of honeypot, monitoring tool, and alerting system for detecting insecure configurations.
A serverless application that creates and monitors fake HTTP endpoints as honeytokens to detect attackers, malicious insiders, and automated threats.
Create and monitor fake HTTP endpoints automatically with Honeyku, deployable on Heroku or your own server.
Galah is an LLM-powered web honeypot that mimics various web applications by dynamically responding to HTTP requests.
A script for extracting network metadata and fingerprints such as JA3 and HASSH from packet capture files or live network traffic.
Deception based detection techniques with MITRE ATT&CK mapping and Honey Resources.
HoneyDB is a honeypot-based threat intelligence platform that provides real-time insights into attacker behavior and malicious activity on networks.
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.
