Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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KQL Query
Detects M365 Copilot jailbreak attempts through prompt injection techniques including rule manipulation, system bypass commands, and AI impersonation requests that attempt to circumvent built-in safety controls. The detection searches exported eDiscovery prompt logs for jailbreak keywords like "pretend you are," "act as," "rules=", "ignore," "bypass," and "override" in the Subject_Title field, assigning severity scores based on the manipulation type (score of 4 for amoral impersonation or explicit rule injection, score of 3 for entity roleplay or bypass commands). Prompts with a jailbreak score of 2 or higher are flagged, prioritizing the most severe attempts to override AI safety mechanisms through direct instruction injection or unauthorized persona adoption.
This rule detects attempts to bypass M365 Copilot's safeguards and extract sensitive or comprehensive information using various social engineering techniques. It specifically looks for keywords in the 'Subject_Title' field of exported eDiscovery prompt logs that indicate requests for classified, proprietary, or bulk data, or attempts to bypass privacy controls. The rule categorizes these attempts by extraction type and assigns severity levels, filtering out low-severity cases to highlight the most critical risks of AI manipulation for data exfiltration.
Detects M365 Copilot impersonation and roleplay jailbreak attempts where users try to manipulate the AI into adopting alternate personas, behaving as unrestricted entities, or impersonating malicious AI systems to bypass safety controls. The detection searches exported eDiscovery prompt logs for roleplay keywords like "pretend you are," "act as," "you are now," "amoral," and "roleplay as" in the Subject_Title field. Prompts are categorized into specific impersonation types (AI_Impersonation, Malicious_AI_Persona, Unrestricted_AI_Persona, etc.) to identify attempts to override the AI's safety guardrails through persona injection attacks.
Detects changes to payment or bank account information in Workday. This activity is associated with 'payroll pirate' attacks by threat actor Storm-2657, who modify payroll details to divert salary payments after compromising an account.
KQL Query
Detects a series of Salesforce Object Query Language (SOQL) queries indicative of data reconnaissance and exfiltration, similar to techniques used by UNC6395.
Detects a user authorizing a Salesforce application via the '/setup/connect' page. This specific TTP is associated with ShinyHunters vishing campaigns where attackers impersonate IT staff and trick employees into entering a connection code, thereby granting a malicious application access to the organization's Salesforce environment.
Detects multiple failed Multi-Factor Authentication (MFA) push attempts for a user from a single IP address, followed by a successful attempt within a short time window. This pattern can indicate an MFA push bombing or fatigue attack where an adversary spams a user with MFA requests until they approve one.
Detects potential exploitation of a critical ABAP code injection vulnerability (CVE-2025-42957) in SAP S/4HANA. An attacker with low-level privileges can leverage a vulnerable RFC module, /SLOAE/DEPLOY, to execute arbitrary code and gain full control of the system.
Detects Salesforce Object Query Language (SOQL) queries that may be attempting to access or exfiltrate sensitive credentials like passwords, API keys, or tokens stored within Salesforce objects. This activity was observed in the UNC6395 campaign where attackers targeted AWS keys, Snowflake tokens, and passwords.
Detects the execution of specific SOQL (Salesforce Object Query Language) queries associated with the UNC6395 data theft campaign. The actor used these queries to enumerate and exfiltrate data from sensitive Salesforce objects.
Detects data theft activity from Salesforce instances by the threat actor UNC6395, who leveraged compromised OAuth tokens associated with the Salesloft Drift third-party application. The detection is based on suspicious user agents and known malicious IP addresses used by the threat actor, specifically targeting the 'Drift' connected application.
Detects the use of known malicious or suspicious User-Agent strings associated with the UNC6395 data theft campaign targeting Salesforce instances.
Detects a user self-assigning access to a large number of applications in Okta within a short time frame. This behavior is associated with the threat group UNC3944 (Scattered Spider) for privilege escalation and lateral movement across SaaS applications.
Detects indicators of compromise (IOCs) and suspicious OAuth consent activity
linked to the Tycoon/ODx phishing kits. These campaigns impersonate Microsoft
OAuth apps to bypass MFA and harvest Microsoft 365 credentials.
linked to the Tycoon/ODx phishing kits. These campaigns impersonate Microsoft
OAuth apps to bypass MFA and harvest Microsoft 365 credentials.
This rule detects suspicious Okta login attempts where a user successfully passes the initial authentication factor but then fails or abandons a subsequent Multi-Factor Authentication (MFA) challenge, originating from a new IP address, device (user agent), and city combination. It filters out events from known corporate VPN IP addresses to reduce false positives.
Detects suspicious activities in Salesforce environments consistent with the TTPs of the ShinyHunters threat group (also tracked as UNC6040/UNC6240). The attack involves social engineering (vishing) to trick users into authorizing malicious OAuth applications that impersonate legitimate tools like 'Salesforce Data Loader'. This rule looks for key indicators such as the installation of unreviewed packages, creation of impersonating connected apps, elevation of permissions to 'full' API access, and large data exports.
Detects potential data exfiltration from cloud storage or SaaS platforms based on a large volume of data transferred or a high number of distinct files accessed by a single user or application within a one-hour window. This is a common TTP for threat actors like ShinyHunters who aim to steal large datasets.
Detects attempts to perform prompt injection attacks against Large Language Models (LLMs) and AI bots. These attacks aim to subvert the model's original instructions, bypass safety mechanisms, or extract sensitive information.
This rule detects the creation of new Microsoft Power Automate flows that are configured to use connectors capable of exfiltrating data via email or collaboration platforms like Microsoft Teams. It specifically looks for flows created with connectors which could indicate an attempt to send sensitive information outside the organization.
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