Ibrahim Saud
@tektrixIndiaCompletionist
2 followers18,458 downloads7,084 copies47 likes38,471 views
11,679 detections
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Detects the execution of 'gitlab-runner.exe' with command-line arguments indicative of runner registration, configuration file interaction, or process execution. This activity may be indicative of legitimate CI/CD pipeline setup or potential persistence mechanism installation if performed by unauthorized accounts.
This rule detects anomalous, high-volume activity in Jira, such as mass ticket viewing, searching, exporting, or attachment downloading, which is often characteristic of unauthorized data exfiltration or reconnaissance. The detection triggers when an account exceeds thresholds of 200 issue-related actions or 8 attachment downloads within a 7-day period.
Detects process execution attempts where the binary is located in specific, non-standard macOS system library paths often associated with background tasks or masquerading, specifically when the process is invoked with a '--type=renderer' command-line argument. This is a common pattern for malicious code attempting to mimic browser-based or helper processes.
Detects the use of PowerShell to download a file named Document.pdf using Invoke-WebRequest, saving it to disk, and immediately executing it using Start-Process. This behavior is indicative of a dropper or secondary stage payload execution pattern.
Detects network connection events associated with the download of machine learning model artifacts from public hubs that display indicators of malicious intent, such as unverified or newly created publisher accounts, missing or invalid cryptographic signatures, or the presence of embedded executable/pickle-deserialization payloads.
Detects network connection events associated with the download of machine learning model artifacts from public hubs that display indicators of malicious intent, such as unverified or newly created publisher accounts, missing or invalid cryptographic signatures, or the presence of embedded executable/pickle-deserialization payloads.
Detects anomalous, high-volume querying against machine learning inference endpoints. The rule identifies potential model extraction or data exfiltration attempts by flagging high counts of requests from a single user/API key, particularly during off-hours or with high variance in unique inputs, which may indicate automated efforts to reconstruct proprietary models or extract sensitive training data.
Detects anomalous, high-volume querying against machine learning inference endpoints. The rule identifies potential model extraction or data exfiltration attempts by flagging high counts of requests from a single user/API key, particularly during off-hours or with high variance in unique inputs, which may indicate automated efforts to reconstruct proprietary models or extract sensitive training data.
Detects the installation of machine learning or AI software dependencies (pip, conda, npm, poetry) that either pull from unauthorized registries or match known malicious/typosquatting naming patterns (e.g., LiteLLM supply-chain compromise). The rule also monitors for newly published packages that have been flagged as suspicious.
Detects potential data leakage from an LLM application where a user repeatedly prompts the model to divulge its internal system instructions, configuration, or sensitive information (e.g., API keys, PII) and the model subsequently outputs that sensitive data.
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