Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network traffic associated with Adversary-in-the-Middle (AiTM) phishing kits that impersonate Microsoft and Google login pages. The rules identify the presence of specific HTML elements (like Cloudflare Turnstile anti-bot gates) and authentication session cookie headers served from non-authorized/non-official domains, indicating a reverse-proxy phishing attack.
Detects anomalous Kerberos TGS-REQ activity (Event ID 4769) from a single user account targeting five or more distinct Service Principal Names (SPNs) using RC4 encryption (0x17). This pattern is consistent with Kerberoasting techniques used by offensive tools like Rubeus, where service tickets are requested for offline cracking to extract plaintext service account credentials.
Detects instances where an IAM user creates a new access key or requests a session token, followed by subsequent API activity from a different source IP address or region within a one-hour window. This behavior is often associated with the creation of persistence mechanisms or credential theft.
This rule detects potential Kerberoasting activity by monitoring Kerberos Service Ticket requests (Event IDs 4768 and 4769) that utilize the weak RC4 encryption type (0x17). The rule filters for non-computer account activity and specific suspicious Ticket Options that are commonly associated with tool-based Kerberoasting attacks.
Detects attempts to dump credentials from the Local Security Authority Subsystem Service (LSASS) process memory using common tools like Mimikatz, Procdump, or built-in system utilities such as comsvcs.dll or command-line arguments indicating memory dump operations.
Detects Kerberos service ticket requests (EventID 4769) that utilize RC4 encryption (0x17) instead of the more secure AES encryption. Attackers often force RC4-HMAC when requesting service tickets for accounts with Service Principal Names (SPNs) because these tickets are susceptible to offline brute-force attacks to recover service account passwords. This technique is a common precursor to lateral movement and privilege escalation in Active Directory environments.
Detects potential Pass-the-Hash (PtH) activity by monitoring for NTLM network logons (Logon Type 3) that are associated with a blank WorkstationName, often indicating spoofing. This pattern is correlated with the assignment of special privileges (EventID 4672) to capture scenarios where stolen NTLM hashes are replayed for lateral movement into a system.
Detects Kerberos service ticket requests (EventID 4769) that utilize RC4 encryption (0x17) instead of the more secure AES encryption. Attackers often force RC4-HMAC when requesting service tickets for accounts with Service Principal Names (SPNs) because these tickets are susceptible to offline brute-force attacks to recover service account passwords. This technique is a common precursor to lateral movement and privilege escalation in Active Directory environments.
Detects high or medium severity security alerts related to Kerberos Golden Ticket forgery, as identified by Microsoft Defender for Identity. This rule filters for specific alert names indicative of forged TGTs minted from the krbtgt account, excluding generic or informational ticket anomalies.
This rule detects repeated access to the RubyGems legacy API key endpoint within a short timeframe from a single source. This behavior is indicative of automated credential harvesting, where an adversary attempts to brute force or cycle through various API keys to gain unauthorized access to RubyGems accounts.
Detects outbound HTTP POST requests containing an aggregated 'LOOT' variable in the body. The rule looks for markers indicating the exfiltration of Kubernetes-related credentials, such as service account tokens, bearer tokens, or configuration secrets, as well as common environment variable dumps like API keys.
This rule monitors for two related scenarios targeting the RubyGems legacy API-key endpoint. First, it identifies suspicious responses indicating an API key might have been served from a CDN cache, potentially leading to cross-user credential leaks. Second, it flags a high volume of requests to the same endpoint using gzip compression, which is a known technique associated with edge-cache poisoning attacks.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
Detects a multi-stage attack chain originating with a healthcare-themed phishing email (e.g., billing or patient coordination lures) that leads to a link click, followed by a subsequent suspicious user sign-in from a non-managed/non-compliant device for the same recipient. This correlates email threat data, URL click telemetry, and sign-in logs to identify potential credential harvesting.
Detects a multi-stage attack chain originating with a healthcare-themed phishing email (e.g., billing or patient coordination lures) that leads to a link click, followed by a subsequent suspicious user sign-in from a non-managed/non-compliant device for the same recipient. This correlates email threat data, URL click telemetry, and sign-in logs to identify potential credential harvesting.
Detects a multi-stage attack chain originating with a healthcare-themed phishing email (e.g., billing or patient coordination lures) that leads to a link click, followed by a subsequent suspicious user sign-in from a non-managed/non-compliant device for the same recipient. This correlates email threat data, URL click telemetry, and sign-in logs to identify potential credential harvesting.
This rule detects unauthorized processes attempting to read sensitive configuration files (secrets.json, config.yaml) associated with AI coding assistants like Cline and Continue. By filtering out known, legitimate IDE and development processes, the rule highlights potential file-grabber activity indicative of an infostealer attempting to exfiltrate plaintext LLM API keys.
Detects process execution patterns where a process, such as an AI agent runtime, accesses sensitive local credential stores, configuration files (.aws, .kube, .ssh, .env), or executes commands to dump environment variables containing sensitive keywords like API_KEY or SECRET. This behavior is indicative of credential harvesting by an agent compromised or misconfigured to exfiltrate host-level secrets.
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