Arnold Chan
@slazTrusted contributorCompletionist
2 followers2,100 downloads4,386 copies51 likes26,184 views
7,555 detections
Filters
Last updated
All Time
Detection languages
5,650
1,006
394
169
98
Categories
2,356
1,807
1,233
1,013
924
Platforms
4,191
1,622
1,250
676
676
Products / Services
2,820
1,003
991
720
485
MITRE Techniques
2,087
1,520
1,413
1,070
536
CVEs
38
33
24
22
21
IDS Classtypes
477
179
104
79
52
IDS Protocols
554
156
144
86
18
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
Detects the execution of RatHat, a Go-based Android native daemon masquerading as 'liblocal-service.so'. The rule identifies the malware's use of ADB-derived shell commands to bypass Android Doze mode, prioritize background execution, and tamper with installed applications via pm (package manager) commands.
Page 313 of 756