Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects HTTP redirect chains originating from common webmail providers to URLs containing suspicious login-related keywords or obfuscated brand look-alike domains, which is indicative of credential harvesting or phishing activity.
Detects a correlation between a DNS query for a potential typosquatting or brand-lookalike domain (newly observed within the last 30 days) and a subsequent sign-in authentication event originating from the same client IP within a 30-minute window. This behavior is indicative of an attacker, such as ShinyHunters vishing operators, directing victims to a lookalike SSO phishing portal to harvest credentials and MFA tokens before attempting access to the legitimate environment.
Detects OAuth application consent grant events where an application name matches common 'Data Loader' naming conventions and requests high-privilege scopes (read/write/full/api). This pattern is consistent with consent phishing campaigns used by threat actors to gain persistent access to SaaS environments like M365 or Salesforce.
Detects high-volume, individually-tailored spear-phishing campaigns likely assisted by Generative AI. The rule monitors for a single sender targeting a high number of unique recipients with distinct, non-identical email subjects within a short timeframe. It specifically flags activity involving newly registered look-alike domains and interactions with credential-harvesting patterns in URLs.
Detects phishing messages that deliberately mix writing systems/scripts or use homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. This is the only translation that implements true sender-novelty checking via baseline lookups (a sending domain must be new to both the specific recipient and the organization as a whole to add risk). A weighted score (script-mixing=2, homoglyph=3, phishing vocabulary=2, sender-novelty=1, threshold=6) requires multiple corroborating signals before firing. Note: unlike the SPL/S1QL/CQL/CortexQL translations, this version does not strip brand/product tokens before the script check.
Detects phishing messages that deliberately mix writing systems/scripts or use homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. This is the only translation that implements true sender-novelty checking via baseline lookups (a sending domain must be new to both the specific recipient and the organization as a whole to add risk). A weighted score (script-mixing=2, homoglyph=3, phishing vocabulary=2, sender-novelty=1, threshold=6) requires multiple corroborating signals before firing. Note: unlike the SPL/S1QL/CQL/CortexQL translations, this version does not strip brand/product tokens before the script check.
Detects phishing messages that deliberately mix writing systems/scripts or use homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. This is the only translation that implements true sender-novelty checking via baseline lookups (a sending domain must be new to both the specific recipient and the organization as a whole to add risk). A weighted score (script-mixing=2, homoglyph=3, phishing vocabulary=2, sender-novelty=1, threshold=6) requires multiple corroborating signals before firing. Note: unlike the SPL/S1QL/CQL/CortexQL translations, this version does not strip brand/product tokens before the script check.
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.
Detects phishing messages that mix writing systems/scripts/homoglyphs in the Subject line as one corroborating signal within a broader, multi-signal phishing pattern.
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 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 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 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 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 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 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 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 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 emails that mix writing systems or scripts in the Subject line, combined with phishing vocabulary.
Detects phishing emails that mix writing systems or scripts in the Subject line, combined with phishing vocabulary.
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