Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,647 detections
Filters
Last updated
All Time
Detection languages
1,768
374
351
273
255
Contributors
908
676
593
359
164
Categories
1,502
1,169
782
528
521
Platforms
39,936
7,266
6,535
3,866
3,647
Products / Services
807
512
432
340
300
MITRE Techniques
1,180
582
528
506
438
CVEs
17
16
9
8
7
IDS Classtypes
41
15
10
6
5
IDS Protocols
47
24
3
2
1
This rule monitors the software inventory for vulnerable versions of OpenSSL and WolfSSL libraries. Specifically, it flags instances of WolfSSL prior to version 5.9.4, which are susceptible to peer-authentication bypass vulnerabilities. The rule is intended for patch management and asset exposure tracking rather than detecting active exploitation.
Detects the execution of known Python scripts (CES_Enroll.py, ntlm_ces_relay.py, ces_negotiate_ntlm.py) often associated with NTLM relay attacks or forced authentication techniques. These scripts leverage Python to interact with authentication protocols or relay requests.
Detects anomalous activity where AI assistant processes (e.g., Claude, ChatGPT, GitHub Copilot) execute a high volume of diverse discovery commands. The rule correlates multiple discovery categories, such as account, network service, system information, and network configuration discovery, occurring within a short timeframe to identify potential abuse of AI-integrated development tools for host reconnaissance.
This rule detects the execution or presence of specific Go or Terraform modules known to be malicious or associated with suspicious dependency retrieval. It monitors command-line activity from terraform.exe/go.exe attempting to download/run from these paths, as well as file system events involving these specific folder paths and filenames.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
The following analytic detects any outbound network connection from an endpoint process to a known suspicious or non-standard port.
It leverages Cisco Network Visibility Module flow data logs to identify potentially suspicious behavior by looking at processes
communicating over ports like 4444, 2222, or 51820 are commonly used by tools like Metasploit, SliverC2 or other pentest, red team or malware.
These connections are worth investigating further, especially when initiated by unexpected or non-network-native binaries.
It leverages Cisco Network Visibility Module flow data logs to identify potentially suspicious behavior by looking at processes
communicating over ports like 4444, 2222, or 51820 are commonly used by tools like Metasploit, SliverC2 or other pentest, red team or malware.
These connections are worth investigating further, especially when initiated by unexpected or non-network-native binaries.
Detects suspicious network activity or credential exfiltration attempts occurring during package manager installation processes (npm, pip, yarn). The rule monitors for lifecycle scripts (e.g., preinstall, postinstall) spawning network utilities to external destinations or accessing common CI/CD secrets like AWS_SECRET_ACCESS_KEY or GITHUB_TOKEN.
Detects unauthorized modifications to the .ssh/authorized_keys file. Such changes are often performed by adversaries to establish persistence by ensuring their own public keys allow them remote access to the host. The rule filters out known configuration management and system tools to reduce noise.
This rule detects potentially malicious behavior in package manager post-install scripts (npm, pip). It monitors for child processes like shells, network utilities (curl, wget), or command-line arguments indicative of credential harvesting (e.g., accessing .ssh, .aws/credentials, environment variables) initiated by package management processes.
This rule detects suspicious process lineage where a Python interpreter executes system commands indicative of a compromised or trojanized pandas package. Specifically, it identifies Python processes spawning command-line utilities for malicious shell operations (e.g., cmd.exe /c calc.exe on Windows or uname -a on Linux/macOS), suggesting an attempt to verify successful arbitrary code execution via a hijacked library function like read_json.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
This rule identifies potential command and control (C2) beaconing activity by detecting periodic, low-jitter outbound network connections. It analyzes network logs to find connections to external IP addresses that maintain a consistent frequency over a period of time, specifically flagging hosts that demonstrate repetitive traffic patterns with low variation (jitter). The rule further filters by identifying 'rare' destinations, focusing on remote IPs that have been connected to by three or fewer hosts within the environment, which is a common indicator of targeted C2 infrastructure.
Detects execution of MeshAgent binaries when initiated by common scripting or application processes (e.g., java, sh, bash, python) or when displaying suspicious command-line patterns indicative of unauthorized remote access or download activity, particularly in environments like WebLogic or PeopleSoft.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
This rule monitors network traffic, DNS queries, and user web clicks to detect interactions with known malicious domains associated with credential harvesting and phishing campaigns, specifically those impersonating security or SSO portals.
Detects the execution of known Python scripts (CES_Enroll.py, ntlm_ces_relay.py, ces_negotiate_ntlm.py) often associated with NTLM relay attacks or forced authentication techniques. These scripts leverage Python to interact with authentication protocols or relay requests.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
Detects usage of the Android Debug Bridge (ADB) 'pair' command initiated by processes other than known legitimate Android development tools (e.g., adb.exe, Android Studio). This may indicate an attacker attempting to wirelessly pair a malicious device or gain unauthorized access to an Android device over the network.
Detects instances where common AI agent orchestration runtimes (e.g., LangChain, AutoGPT, CrewAI) spawn command-line or scripting interpreters as child processes. This behavior is highly suspicious and often indicative of malicious command execution triggered by prompt injection attacks, where an adversary manipulates the agent into executing arbitrary system commands.
Detects the installation of known AI-powered coding assistant extensions (e.g., Copilot, Tabnine, Cursor) on a device, followed by significant source-code repository cloning or archival activity by Git within two hours. This pattern is potentially indicative of intellectual property theft or sensitive source code exfiltration facilitated by a newly introduced AI tool.
Page 11 of 174