Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; a weighted score (script-mixing=2, homoglyph=3, high-confidence phishing vocabulary=2, threshold=6) means no two of the three signal categories can reach the threshold alone — all three must co-occur before the rule fires.
Detects phishing messages that mix writing systems/scripts/homoglyphs in the Subject line as one corroborating signal within a broader, multi-signal phishing pattern.
This rule detects potential phishing attempts where the sender's display name is formatted as an email address and the sender's domain uses a capital 'I' character to mimic a well-known service provider (typosquatting/homoglyph attack), such as 'gmaiI.com'. It identifies discrepancies between the actual sender domain and the normalized domain name after character replacement.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
For years, when hunting phishing emails, I have mainly focused on what the user sees inside of it. But I think AI agents are going to change this.
An email may soon have two audiences: Human + AI Agent. And they may not always see exactly the same thing.
In this new KQL, I explore a simple example: invisible Unicode characters. Microsoft and Micro<U+200B>soft may look the same to us, but not necessarily to a detection, filter, parser or AI agent. For attackers, this difference can be interesting as a way to obfuscate content and potentially evade keyword-based detections or other systems processing the text.
I hunt these patterns in email and add context around them. Because attackers will not only write emails for us. They will(are) start writing emails for our agents too.
An email may soon have two audiences: Human + AI Agent. And they may not always see exactly the same thing.
In this new KQL, I explore a simple example: invisible Unicode characters. Microsoft and Micro<U+200B>soft may look the same to us, but not necessarily to a detection, filter, parser or AI agent. For attackers, this difference can be interesting as a way to obfuscate content and potentially evade keyword-based detections or other systems processing the text.
I hunt these patterns in email and add context around them. Because attackers will not only write emails for us. They will(are) start writing emails for our agents too.
This rule identifies potential infections associated with the MacSync malware family by matching process, file, and network indicators (hashes, malicious domains, and specific payload URLs) against endpoint telemetry. It monitors for execution of known malicious binaries, file creation events, and communication with identified command-and-control infrastructure.
Detects anomalous sign-in behavior using the OAuth 2.0 device code authorization flow. The rule identifies sign-ins from previously unseen geographic locations or IP ranges (ASN) for a specific user, or detects a potential tenant-wide phishing campaign where multiple users are performing device-code authentications in a short timeframe, which is characteristic of 'device code phishing' (a form of OAuth consent phishing).
Detects anomalous sign-in behavior using the OAuth 2.0 device code authorization flow. The rule identifies sign-ins from previously unseen geographic locations or IP ranges (ASN) for a specific user, or detects a potential tenant-wide phishing campaign where multiple users are performing device-code authentications in a short timeframe, which is characteristic of 'device code phishing' (a form of OAuth consent phishing).
Detects mass-phishing consent campaigns by monitoring non-admin users consenting to multi-tenant or unverified-publisher applications requesting non-basic-profile OAuth scopes. The rule aggregates multiple consent events for the same application over a 1-hour window to identify potentially malicious consent-phishing activity.
Detects phishing emails that impersonate Adobe Acrobat Sign or DocuSign using invoice or RFP-themed lures. The rule identifies suspicious emails by checking for brand-related keywords in the subject and display name, identifying links that do not lead to legitimate signing domains, and identifying senders that are impersonating a brand or are newly seen in the environment.
Detects inbound phishing emails containing brand mentions for Signal, WhatsApp, or Telegram alongside lure phrases related to verification, 2FA, or device linking. The rule specifically filters for emails containing URLs while excluding messages originating from official domains associated with these services.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
Detects HTTP response bodies containing indicators of 'ClickFix' social engineering lures. The rule monitors for JavaScript code designed to manipulate the user's clipboard (execCommand('copy') or clipboard.writeText) in conjunction with instructions for the user to execute the Windows Run dialog (Win+R), a common technique to trick users into executing malicious commands under the guise of solving a fake CAPTCHA or error.
This rule detects established TLS connections to external domains that exhibit characteristics often associated with phishing campaigns. Specifically, it looks for domain names in the SNI field that contain common brand names (e.g., microsoft, google, paypal) combined with typical login-related keywords (e.g., login, verify, auth) on high-churn or suspicious Top-Level Domains (TLDs) such as .top, .xyz, or .icu.
This rule performs a retrospective sweep for indicators of compromise (IOCs) associated with the 'CSuite' phishing and RMM (Remote Monitoring and Management) campaign. It monitors network, DNS, proxy, email, URL click, and file creation telemetry over the past 24 hours to identify interactions with known-malicious IP addresses, domains, URLs, and file hashes related to the campaign.
This rule performs a retrospective sweep for indicators of compromise (IOCs) associated with the 'CSuite' phishing and RMM (Remote Monitoring and Management) campaign. It monitors network, DNS, proxy, email, URL click, and file creation telemetry over the past 24 hours to identify interactions with known-malicious IP addresses, domains, URLs, and file hashes related to the campaign.
Detects incoming email messages where the Reply-To header address domain differs from the Sender From address domain. This technique is commonly used in phishing attacks to redirect victim replies to an attacker-controlled mailbox while maintaining a spoofed appearance in the original sender field.
This rule identifies potential infections associated with the MacSync malware family by matching process, file, and network indicators (hashes, malicious domains, and specific payload URLs) against endpoint telemetry. It monitors for execution of known malicious binaries, file creation events, and communication with identified command-and-control infrastructure.
This rule detects inbound email messages where the SPF check passes for a specific domain (osc-cdn.com) or uses specific infrastructure (Migadu), but the DMARC check fails due to misalignment between the envelope domain and the From header. This pattern is indicative of email spoofing or domain impersonation attempts where the attacker abuses misconfigured SPF/DMARC policies (p=none) to facilitate spearphishing.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
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