Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 suspicious account management activities, such as password resets or MFA re-enrollment, performed by an operator on a high-privileged or administrator account outside of typical business hours (06:00-20:00). This behavior is often indicative of help-desk impersonation or credential takeover attempts targeting Identity Provider (IdP) administrative roles.
Detects a suspicious sequence of events indicating account takeover: a password reset, followed by an MFA factor re-enrollment, and concluding with a successful login from a geographically impossible location compared to recent history. This pattern often signifies a help-desk-assisted account compromise or token theft.
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.
Detects phishing emails that mix writing systems or scripts in the Subject line, combined with phishing vocabulary.
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