Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,143 detections
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Detects a suspected 5-step exploit sequence targeting CVE-2026-43503 on Linux systems. The rule monitors for unprivileged user and network namespace creation, followed by XFRM state modification, kernel module loading, raw ESP socket creation, and finally writing to /proc/[pid]/mem to facilitate page-cache corruption and bypass Copy-on-Write (CoW) protections.
This rule monitors process command lines and file activity for keywords associated with potential reconnaissance, error logs, or debugging artifacts often used during post-exploitation or system profiling. It flags commands or files containing terms like 'memory dump', 'stack trace', 'SQL injection', or 'Redis failure', which may indicate an attacker analyzing system information or attempting to interact with backend services.
Detects the execution of Dropbear SSH binaries (dropbear, dropbearkey, dropbearmulti) on identified OT, ICS, or SCADA assets. The rule flags suspicious configurations such as the use of non-standard ports (2222, 44818), remote port forwarding, or running in the foreground, which may indicate unauthorized remote access or tunneling on sensitive operational technology infrastructure.
Detects a sequence of events where a non-root user attempts to manipulate kernel components (specifically involving xfrm/esp modules via ip or modprobe) followed immediately by a sudo or su command, suggesting an attempt to load malicious kernel modules or exploit kernel vulnerabilities to escalate privileges.
Detects suspicious execution of Node.js processes initiated by VS Code (or Cursor) that appear to be running JavaScript payloads disguised as font files (.woff2) or other suspicious assets. It also monitors for the creation or modification of '.vscode/tasks.json' files by package managers or source control tools, which could indicate the configuration of malicious 'folderOpen' tasks used for persistence or automated execution upon workspace opening.
This rule detects various Linux persistence mechanisms, including the use of LD_PRELOAD to hijack dynamic linker execution, the creation or modification of malicious cron jobs, and tampering with shell startup scripts. These techniques allow an adversary to maintain access to a compromised host by ensuring malicious code is executed automatically or injected into legitimate processes.
Detects host-level configuration changes indicative of preparing a system for the CVE-2026-46331 pedit COW exploit. This includes enabling unprivileged user namespaces via sysctl and disabling security mitigations or removing the act_pedit kernel module.
Detects anomalous process spawning behavior initiated by AI IDE extensions (e.g., Amazon Q, Claude Code, Cursor) when loading Model Context Protocol (MCP) configuration files. The rule flags instances where these IDE components trigger suspicious child processes or command-line executions, which could be indicative of remote code execution (RCE) via malicious mcp.json workspace configurations.
Detects malicious attempts to impair system defenses on Linux hosts, specifically targeting AppArmor, SELinux, firewalls (UFW/iptables), and security monitoring agents (Crowdstrike/osquery/Aliyun) by the 'lambsys' cryptominer campaign. This behavior aligns with adversary attempts to evade detection and maintain persistence or resource consumption.
This rule detects potentially unauthorized installation of extensions in Visual Studio Code (VSIX) or plugins in JetBrains IDEs (IntelliJ, PyCharm, WebStorm). It flags installations that do not originate from verified official marketplaces, which may indicate the manual installation of malicious or supply-chain compromised development environment extensions.
Detects evidence of active exploitation targeting the libarchive ZIP entry declared-vs-actual size boundary vulnerability. The rule identifies the execution of known proof-of-concept (PoC) scripts (make_debuginfod_zip.py, run_stock_marker.py) as well as the creation of specific marker files or CLI output from bsdunzip/bsdtar that confirm successful boundary exploitation.
Detects the presence of specific file artifacts ('VULNERABILITY_CONFIRMED.marker', 'smtp-crlf-injection.curlrc') associated with the 'curl-smtp-expn-recipient-crlf-injection' proof-of-concept repository. The rule also correlates this with the execution of the associated 'run_demo.py' script from directories containing 'curl-smtp', helping identify active use of this testing framework.
Detects the presence of known malicious Chrome extension IDs, suspicious configuration files (e.g., perplexity-rules.json, bing-rules.json), or extensions utilizing invasive declarativeNetRequest permissions, which are often used for browser-based exfiltration or credential interception.
Detects suspicious subprocesses spawned by IDE or extension host processes (like VS Code or Amazon Q) that execute AWS CLI commands to enumerate identity or immediately exfiltrate data via curl/wget, indicating potential credential abuse by malicious plugins or MCP servers.
Detects activity indicative of container escape attempts, specifically identifying the use of 'docker cp' with path traversal sequences to manipulate host files, or the unauthorized execution of shells and utilities directly spawned by the container runtime daemon (dockerd). It also monitors for execution patterns associated with known container escape proof-of-concept scripts.
Detects anomalous or unauthorized processes accessing sensitive AI configuration files, SSH configurations, version control files, and shell history within a short time window. The rule flags instances where non-standard or unexpected applications exhibit broad or high-frequency access to these files, which are common targets for credential harvesting or exfiltration by adversaries.
Detects potential container breakout or privilege escalation attempts originating from Gitea act-runner processes. The rule monitors for the execution of containers with dangerous security configurations (e.g., host PID/IPC namespace, privileged capabilities, disabled security modules) or the use of nsenter to break out of a container into the host environment.
Detects a multi-stage attack where an adversary reads local AWS credentials or configuration files (e.g., .aws/credentials, mcp.json) on a developer workstation and subsequently performs unauthorized AWS API calls from the same source IP. The rule correlates initial credential access on the endpoint with a sequence of AWS CloudTrail events starting with identity verification (GetCallerIdentity) followed by enumeration and potentially high-impact API calls (e.g., RunInstances, AssumeRole).
Detects a Node.js process, spawned as a child of an IDE (e.g., VS Code, Cursor), that accesses local AWS configuration or credential files followed by an outbound network connection to a non-AWS destination. This behavior is indicative of potential cloud credential exfiltration via malicious Model Context Protocol (MCP) server execution.
Detects the creation of Model Context Protocol (MCP) server configuration files (such as .mcp.json or those in .vscode/ and .amazonq/ directories) shortly after a Git operation (pull, fetch, merge, or clone). This pattern is indicative of a supply chain attack where a contributor introduces malicious MCP configurations into a workspace, which are subsequently loaded by development tools like VS Code, Cursor, or language servers to execute arbitrary code or exfiltrate data.
Detects the use of the 'docker cp' command with suspicious path traversal characters or known PoC artifacts in the destination parameter. This behavior is indicative of an attempt to exploit path traversal vulnerabilities in the Docker daemon (such as those allowing container escape) by writing files outside the container's intended scope onto the host filesystem.
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