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 Jul 2026. Is something off? Reach out.
FEATURED
USE CASES
A multiarch honeypot platform supporting 20+ honeypots and offering visualization options and security tools.
A low-interaction SSH honeypot that logs connection attempts, usernames, and passwords without allowing actual login access.
A fake Django admin login screen to detect and notify admins of attempted unauthorized access
Kippo is a medium interaction SSH honeypot with fake filesystem and session logging capabilities.
A command-line tool for analyzing Cowrie honeypot log files over time, generating statistics and visualizations from local or remote log data.
A project providing honeypots for embedded device vulnerabilities with support for AWS integration and JSON output.
A honeypot installation for Drupal that supports Go modules and mimics different versions of Drupal.
Medium interaction SSH Honeypot with multiple virtual hosts and sandboxed filesystems.
A high-interaction honeypot system supporting the Redis protocol.
A low interaction honeypot for detecting CVE-2018-0101 vulnerability in Cisco ASA component.
Pure Python implementation of Microsoft RDP protocol with various tools and support for different security layers.
Emulates Docker HTTP API with event logging and AWS deployment script.
A FTP honeypot tool for detecting and capturing malicious file upload attempts.
A honeypot for remote file inclusion (RFI) and local file inclusion (LFI) using fake URLs to catch scanning bots and malwares.
Python-based web server framework for setting up fake web servers and services with precise data responses.
bap is a webservice honeypot that logs HTTP basic authentication credentials.
A medium-interaction PostgreSQL honeypot with configurable settings
A Perl honeypot program for monitoring hostile traffic and wasting hackers' time.
Port listener / honeypot in Rust with protocol guessing, safe string display and rudimentary SQLite logging.
SMTP Honeypot with custom modules for different modes of operation.
Tango is a set of scripts and Splunk apps for deploying honeypots with ease.
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.
