Early Warning Honey Tokens
Honey tokens (also known as canary tokens) are decoy material in the form of credentials or sensitive information, placed where attackers search during reconnaissance and triggering alerts when accessed or used.
Attackers searching a compromised environment tend to look for anything useful - credentials that grant access, documents that reveal infrastructure, configuration files that expose secrets. They enumerate systematically, harvesting what they find and validating what looks promising before using it for lateral movement.
Honey tokens exploit this behaviour by placing decoy material in the locations attackers search. AWS access keys that authenticate to monitored infrastructure, documents that phone home when opened, configuration files that trigger alerts when read. The attacker interacts with what they found, and you learn rather quickly that someone is inside your environment.
Why does early warning detection matter?
Mandiant's M-Trends 2025 report found that 57% of organisations discovered their breaches through external notification rather than internal detection. Fourteen percent learned they were compromised when attackers sent ransom notes. Despite continued investment in EDR, NDR, and SIEM, attackers operating with valid credentials tend to look rather identical to authorised users.
Infostealer malware harvests credentials that remain valid for years. The Snowflake breach used credentials stolen in 2020 - nearly four years of undetected exposure. Honey tokens can detect at the moment of credential validation, before lateral movement begins.
Early Warning Detection for Credential Theft: Why Behavioral Analysis Fails
Rad Kawar / 7m
How do you get started?
Every honey token follows four steps: create, place, monitor, respond. Generate credentials that authenticate to infrastructure you control, place them in locations where attackers conducting reconnaissance would naturally find them, monitor for any authentication attempts, and respond when someone uses credentials that have no legitimate business purpose.
Placement tends to matter more than sophistication. A simple AWS access key in a configuration file that an attacker actually finds and validates provides more value than an elaborate deception that never gets triggered. Starting with locations your threat model suggests attackers would search - code repositories, CI/CD pipelines, internal documentation, developer workstations - and expanding coverage based on what you learn from interactions is generally a sensible approach.
Getting Started with Early Warning Honey Tokens
Rad Kawar / 6m
What makes a good honeytoken?
A good honeytoken gets used. If adversaries discover tokens and skip past them, the deployment hasn't achieved terribly much regardless of how many have been scattered across the infrastructure. This rather reframes the design question: rather than asking how to hide tokens, it's worth considering how to make them attractive enough that adversaries actually validate them.
The most effective honeytokens tend to borrow trust from established services. When an adversary finds an AWS access key, they're seeing Amazon's infrastructure and a validation process they already know rather well. This borrowed trust short-circuits caution - the process is familiar, the risk feels manageable, and the potential reward is clear. Tokens that leverage existing trust relationships get validated considerably more frequently than those requiring adversaries to work out new systems.
What's in a Good Honeytoken
Rad Kawar / 5m
What are the security considerations?
Honey tokens are real credentials that authenticate to real infrastructure, which means they carry security implications that warrant careful consideration. The credentials ought to be sufficiently limited in permission that an attacker who uses them cannot cause damage, whilst remaining sufficiently realistic that they appear worth validating. This balance requires understanding both what attackers expect to find and what access levels your infrastructure can safely expose.
Implementation details tend to matter rather more than one might expect. How credentials are generated, how authentication events are captured, how alerts are delivered, and how the monitoring infrastructure itself is secured all affect whether honey tokens provide reliable detection or create additional attack surface. Organisations deploying honey tokens would do well to understand the technical architecture underlying their solution and the failure modes that could result in missed alerts or exploitable weaknesses.
AWS Honey Tokens: The Good, the Bad, and the Ugly
Rad Kawar / 5m
How does AI change the equation?
AI orchestration frameworks are compressing attack timelines from weeks to hours. Anthropic documented a Chinese state-sponsored campaign that automated 80-90% of tactical operations using AI, with the system making thousands of requests per second at speeds no human team could match. Reconnaissance that previously took a week now completes in hours. Credential validation that happened selectively over days occurs systematically in minutes.
This acceleration makes early warning detection rather more important than it might otherwise be. When attacks move at machine speed, detection that arrives hours or days after initial access provides limited value. Detection at the moment of credential validation - before lateral movement begins - provides the time window necessary to contain threats before they propagate. The faster attacks become, the more valuable it becomes to detect them at the earliest possible point.
AI-Orchestrated Attacks: Why Detection Speed Matters More Than Ever
Rad Kawar / 12m
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