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Password-spraying remains one of the simplest and most effective ways attackers try to gain access to cloud identities. Rather than brute-forcing one account until it locks, adversaries attempt a small set of common passwords across many accounts, staying under lockout thresholds and blending with normal authentication noise. That makes detection by volume or timing difficult and prone to false positives when you rely only on correlating failed sign-ins. (See TrustedSec’s write-up on how they used a honeypot account for Entra ID.)
Below we expand on what password-spraying is, explain how a honeypot/honeynet account helps detect it, and how Labyrinth turns that technique into a scalable, enterprise-grade defense.
According to TechTarget:
Password spraying is a cyberattack tactic that involves a hacker using a single password to try and break into multiple target accounts. It’s a type of brute-force attack.
Some key characteristics:
Attackers try one or a few common passwords (e.g. “Password123”, “Welcome1”, seasonal or default passwords) across many user accounts.
Because each password is used only once per account (or only a few tries per account), it avoids triggering lockout thresholds that would occur in a traditional brute-force against a single account.
Attackers typically assemble their username list via OSINT, leaked credential sets, company directory (or inferred naming conventions), then automate the process.
Because the approach is “low-and-slow” and distributed across many accounts, it tends to evade naïve anomaly detection systems that look for concentrated failed-login spikes.
Also, in the MITRE ATT&CK framework it’s formally categorized under technique T1110.003 (Brute Force – Password Spraying), i.e. a recognized brute-force style credential access attack.
Because attackers keep the per-account attempt volume low (and spread over time or distributed sources), traditional threshold-based detection (many failures on one account) can miss it or trigger too late.
Finally, the impact is real: nation-state backed groups (such as Midnight Blizzard / Nobelium) have used password-spraying as part of larger campaigns. techtarget.com
Log-based detection is reactive and often noisy: many failed logins happen for legitimate reasons (typos, mis-configured services, user errors). Alarm-fatigue is common.
Attackers can evade pattern-based detection by distributing attempts over time, from multiple IPs or VPNs, or throttling their activity.
Deception injects intentional false resources (decoy accounts, fake services, or fake identity endpoints) that legitimate users don’t use, so any interaction is intrinsically suspicious.
That gives you a signal with far lower false positives, and often earlier in the attack chain, than relying solely on log-threshold triggers.
Using a honeypot account is a great start. But to operate deception at scale, with automation, investigation tooling, and response orchestration, that’s where Labyrinth’s Deception Platform adds real value.
Here are the ways Labyrinth advances the concept:
Scale & Realism: Labyrinth doesn’t just create a single “honeypot user”, it deploys believable decoy identities, fake services or service-account-style decoys, integrated into your identity environment in ways attackers expect. You can scale from dozens to hundreds of decoys with plausible identity metadata, roles, and naming patterns.
High-Interaction Decoys: Rather than only logging attempts, Labyrinth’s decoy artifacts can simulate interactions (e.g. decoy management interfaces, fake admin portals or asset indicators). Attackers may reveal tactics, techniques, lateral-movement attempts, letting you gather richer behavioral telemetry.
Management Console & Map-View: Alerts triggered by decoys are surfaced in Labyrinth’s console, vizualised in a map-style view that helps you trace attacker origin, sequence of events, and geolocation, even among many decoy artifacts. This reduces triage time and supports forensic timelines.
Automated Response & Integrations: Not only can decoy-triggered events feed into your SOC / SIEM, but Labyrinth supports integrations (EDR, firewalls, orchestration platforms) to automate blocks (IPs, sessions), quarantine hosts, or trigger further investigation steps.
Attack-vector validation: Because decoys can be modeled after your identity environment (e.g. service accounts, administrative users, privileged roles), you can test whether attackers are probing your specific identity surface. In the case of Entra ID / AAD / Microsoft 365 identity services, you can validate whether password-spray probes are targeting service-type accounts, cloud-admin roles, etc. That helps you gauge your current posture and adjust policies (e.g. MFA, lockout thresholds, anomaly detection rules).
This all means: when an attacker sprays passwords instead of just triggering a threshold alert on many failures, you get early, low-noise, high-context visibility into their reconnaissance or initial breach attempts and you can respond before real accounts are compromised.
Password-spraying remains a widely used attack technique precisely because it is low-cost, effective, and easy to automate. Attackers prefer it over more noisy or noticeable methods.
Relying solely on reactive log-based defense can allow attackers to probe your identity environment silently for days or weeks.
By injecting purpose-built deception into your identity infrastructure (rather than relying only on real user accounts), you gain a proactive, intentional signal.
Labyrinth’s Deception Platform allows you to operationalize this at scale is turning the honeypot concept into a mature detection-and-response capability for cloud identity (Entra ID / Microsoft 365 / AAD) environments.