Ibrahim Saud
@tektrixIndiaCompletionist
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11,679 detections
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Detects high-privilege user accounts (e.g., executives, administrators) undergoing a credential or MFA reset, followed by an atypical sign-in event within a two-hour window. This pattern is indicative of potential account takeover or identity-compromise scenarios, often associated with help-desk vishing attacks where an attacker induces a user or administrator to reset credentials or MFA methods.
Detects network connections from server or workload identities (excluding SYSTEM) to known public Generative-AI inference API domains. This may indicate the use of AI services for data exfiltration, automated content generation, or C2 communication via legitimate web services.
Detects a two-stage activity: first, the potential unauthorized reading or access of LLM provider API keys from files, environment variables, or command-line arguments; and second, the subsequent use of those same credentials to authenticate to LLM provider APIs (OpenAI, Anthropic, Azure, Bedrock) from a different, unrecognized source IP address, indicating potential credential theft and session hijacking.
Detects potential exploitation of AI agent frameworks, such as Semantic Kernel, where plugin or tool invocation primitives are abused to execute arbitrary commands or manipulate files via path traversal. The rule monitors for AI-hosting processes (e.g., python.exe, node.exe) spawning shell child processes or file-related commands containing path traversal sequences (e.g., '..\..\') paired with file operation keywords.
Detects high-privilege OAuth application consent grants occurring shortly after a user identity recovery event (e.g., password or MFA reset). This pattern is indicative of a help-desk social engineering attack where an adversary uses an identity reset to gain access to an account and subsequently establishes persistent access via a malicious OAuth application.
Detects MFA or credential reset activities on high-value or privileged accounts that lack a corresponding out-of-band verification signal, such as manager approval or identity proofing, within a one-hour window. This behavior is indicative of social engineering attempts where attackers manipulate help-desk procedures to bypass identity protections.
Detects Azure AD sign-in events where the user-agent matches common scripting or automation libraries such as python-requests. This behavior is often indicative of automated token replay attacks, where attackers attempt to leverage stolen session cookies or tokens (e.g., following AiTM phishing) via proxy infrastructure.
Detects the execution of command-line interpreters (cmd, powershell, mshta, etc.) directly spawned by explorer.exe. This activity is indicative of the 'ClickFix' or 'ConsentFix' attack chain, where a victim is coerced into pasting malicious commands into the Windows Run dialog (Win+R) after interacting with a fraudulent CAPTCHA or verification page in their browser. The rule filters out standard installer-related activity to reduce false positives.
Detects execution of rundll32.exe or regsvr32.exe when spawned by powershell.exe or mshta.exe, specifically targeting DLLs located in common user-writable or temporary directories (AppData, ProgramData, Downloads). This pattern is consistent with the delivery of CastleLoader shellcode loaders following ClickFix-style social engineering lures.
Detects the use of legitimate Windows system binaries (LOLBins) such as curl.exe, certutil.exe, or bitsadmin.exe to download files. These binaries are monitored when spawned directly from common entry-point processes like explorer.exe, cmd.exe, or powershell.exe, which is characteristic of second-stage payload retrieval in ClickFix social engineering campaigns.
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