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 219 Honeypots & Deception tools, 193 free and 26 commercial.
Accuracy and depth improve over time. Last reviewed Aug 2026. Is something off? Reach out.
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New to this category? What is Honeypots & Deception?
Deception platform that diverts attackers & provides threat intelligence
AI-driven deception platform using honeypots and decoys to detect threats.
Adversary engagement & deception platform for detecting advanced threats
Deception platform using external-facing decoys for threat intel & recon detection
AI-powered deception platform for cloud threat detection using honeytokens
AI-powered deception platform for early APT and advanced threat detection
Credential-based deception platform that lures attackers to capture stolen creds
AI-based deception platform for collecting cyber threat intelligence
AI-powered deception platform using honeypots to detect & disrupt attacks
Cloud-native deception platform deploying dynamic security canaries
Cross-platform HTTP honeypot that traps bots with infinite data streams
A signature-based, multi-threaded honeypot detection tool written in Golang that identifies honeypots through crafted requests and response analysis.
TANNER is a remote data analysis service that evaluates HTTP requests and generates responses for SNARE honeypots while emulating application vulnerabilities.
Fake protocol server simulator supporting 50+ network protocols for deception
The DShield Raspberry Pi Sensor is a tool that turns a Raspberry Pi into a honeypot to collect and submit security logs to the DShield project for analysis.
A security framework for process isolation and sandboxing based on capability-based security principles.
KFSensor is an advanced Windows honeypot system for detecting hackers and worms by simulating vulnerable system services.
A collection of tools that can be used with Honeyd for data analysis or other purposes
High interaction honeypot solution for Linux systems with data control and integrity features.
Building Honeypots for Industrial Networks using Honeyd and simulating SCADA, DCS, and PLC architectures.
A crawler-based low-interaction client honeypot for exposing website threats.
LaBrea is a 'sticky' honeypot and IDS tool that traps malicious actors by creating virtual servers on unused IP addresses.
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.
