Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,143 detections
Filters
Last updated
All Time
Detection languages
3,227
1,086
498
478
469
Contributors
1,824
1,607
872
505
300
Categories
2,979
2,473
1,063
1,026
910
Platforms
39,801
7,143
6,519
3,861
3,600
Products / Services
1,325
1,070
970
655
490
MITRE Techniques
2,231
1,319
1,319
876
873
CVEs
66
65
33
32
28
IDS Classtypes
81
79
56
27
10
IDS Protocols
149
98
6
5
4
Detects the installation of known malicious npm or PyPI packages followed within 30 minutes by the execution of a package manager (node or python) performing network activity. This pattern matches the behavior of downloader stages in the 'Graphalgo' campaign, which uses coding-task lures to trick users into installing malicious packages.
This rule detects potential exploitation attempts targeting vulnerabilities within the libheif image processing library. It monitors for image processing binaries (e.g., convert, magick) executing with command-line arguments related to HEIF/HEIC files, followed by multiple application crash events (SIGSEGV, coredumps) associated with libheif on the same device within a short time window.
This rule detects potential exploitation attempts targeting vulnerabilities within the libheif image processing library. It monitors for image processing binaries (e.g., convert, magick) executing with command-line arguments related to HEIF/HEIC files, followed by multiple application crash events (SIGSEGV, coredumps) associated with libheif on the same device within a short time window.
This rule detects potential exploitation attempts targeting vulnerabilities within the libheif image processing library. It monitors for image processing binaries (e.g., convert, magick) executing with command-line arguments related to HEIF/HEIC files, followed by multiple application crash events (SIGSEGV, coredumps) associated with libheif on the same device within a short time window.
This rule detects potential exploitation attempts targeting vulnerabilities within the libheif image processing library. It monitors for image processing binaries (e.g., convert, magick) executing with command-line arguments related to HEIF/HEIC files, followed by multiple application crash events (SIGSEGV, coredumps) associated with libheif on the same device within a short time window.
Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects malicious shell commands attempting to perform supply-chain persistence via environment variable manipulation in CI/CD environments. The rule identifies processes injecting BASH_ENV into GITHUB_ENV, a known technique used by the SCKIT supply-chain worm to perform credential harvesting during CI pipeline execution.
Detects malicious shell commands attempting to perform supply-chain persistence via environment variable manipulation in CI/CD environments. The rule identifies processes injecting BASH_ENV into GITHUB_ENV, a known technique used by the SCKIT supply-chain worm to perform credential harvesting during CI pipeline execution.
Detects the execution of the 'sckit' Go implant, a malicious binary associated with trojanized '@memtensor/memos-cloud-openclaw-plugin' or 'MemoryOS' packages. The rule identifies instances where this binary is spawned as a child process of a language runtime (Node.js or Python) when the execution originates from specific directory paths associated with these packages, or when the parent process command line indicates these packages are being initialized.
Detects high-volume outbound network scanning activity from a single compute resource, characterized by a significant number of distinct destination IP addresses and ports within a short timeframe. This behavior is indicative of automated service discovery and reconnaissance, often associated with adversary-orchestrated exploitation pipelines or botnet activity.
Detects automated searching (via grep, findstr, or select-string) for sensitive strings (e.g., API keys, private keys, wallet data) across multiple occurrences on a single host, or the creation of suspicious sensitive files (e.g., .env, credentials, wallet.dat) on devices where such automated sweeping activity has been observed. This pattern indicates an adversary staging data for exfiltration.
This rule detects command-line activity where a remote resource is downloaded via 'curl', decoded using 'base64', and then piped directly into a shell interpreter (sh, bash, or zsh). This pattern is commonly used in fileless execution or stage-one payload delivery by attackers to execute malicious scripts directly from memory.
Detects potential JWT algorithm-confusion or authentication-bypass attempts
targeting WSO2 API Manager components through JWTs specifying unsupported,
downgraded, or unexpected signing algorithms such as "none" or HS256 where
asymmetric signing is expected. Matching events should be investigated to
determine whether the request represents exploitation of CVE-2026-5430.
targeting WSO2 API Manager components through JWTs specifying unsupported,
downgraded, or unexpected signing algorithms such as "none" or HS256 where
asymmetric signing is expected. Matching events should be investigated to
determine whether the request represents exploitation of CVE-2026-5430.
Detects Python processes (python.exe, python3.exe, pythonw.exe) performing DNS resolution for Microsoft identity, device login, or Graph API endpoints. This activity is consistent with automated tooling, such as python-requests, performing device-code flow polling or API token authentication within an environment.
Detects access to sensitive browser credential storage files (Login Data, key4.db, logins.json) by unauthorized processes. This behavior is indicative of credential theft, where an adversary attempts to extract stored passwords from browser profiles.
Detects access to sensitive browser credential storage files (Login Data, key4.db, logins.json) by unauthorized processes. This behavior is indicative of credential theft, where an adversary attempts to extract stored passwords from browser profiles.
This rule detects instances where the Terraform CLI tool connects to suspected lookalike domains associated with supply chain compromise attempts (e.g., hashicorp-aws[.]com or hashicorp-terraform[.]io) during common initialization or application workflows. It correlates process execution with network connection logs and optionally validates the presence of a related .terraform.lock.hcl file associated with the malicious origin.
This rule detects instances where the Terraform CLI tool connects to suspected lookalike domains associated with supply chain compromise attempts (e.g., hashicorp-aws[.]com or hashicorp-terraform[.]io) during common initialization or application workflows. It correlates process execution with network connection logs and optionally validates the presence of a related .terraform.lock.hcl file associated with the malicious origin.
Detects browser extension manifest.json files requesting declarativeNetRequest and content_scripts/host_permissions covering AI assistant vendor domains (BragJack attack class)
Detects anomalous process execution originating from the PaperCut application server process, specifically targeting the SetupCompleted servlet endpoint. This pattern is indicative of an exploitation attempt, such as CVE-2026-81578, where unauthenticated requests trigger backend server actions before proper access validation.
Detects SVG image files that contain embedded HTML or script payloads, often used as an initial delivery vector to smuggle malicious code (such as downloaders or web redirects) into a target environment. This technique has been associated with the delivery of various remote access trojans (RATs).
Page 56 of 341