Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,154 detections
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Detects potential exploitation of a chained vulnerability attack targeting Ivanti EPMM 12.5.0.0. CVE-2025-4427 allows unauthenticated access to protected API endpoints via an authentication bypass, which can then be leveraged to trigger CVE-2025-4428 — a remote code execution vulnerability through template injection. This sequence enables unauthenticated remote code execution, significantly increasing the impact of exploitation.
This analytic detects Lumma Stealer download attempts using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with specific IDs have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer.
Detects potential exploitation attempt of CVE-2023-1389, an Unauthenticated Command Injection vulnerability in TP-Link Archer AX21 routers. The rule looks for specific URI patterns and keywords indicative of an attempt to inject commands via the 'country' parameter.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
This analytic identifies Snort signatures that have triggered only once in the past 7 days across all Cisco Secure Firewall IntrusionEvent logs. While these rules typically do not trigger in day-to-day network activity, their sudden appearance may indicate early-stage compromise, previously unseen malware, or reconnaissance activity against less commonly exposed services. Investigating these outliers can provide valuable insight into new or low-noise adversary behaviors.
The analytic identifies ICMP traffic to external IP addresses with total bytes (sum of bytes in and bytes out) greater than 1,000 bytes. It leverages network traffic data to detect large ICMP packets that aren't blocked and are directed toward external networks. This can indicate information smuggling, covert communication, or command-and-control (C2) activities, potentially signaling ICMP tunneling, unauthorized data transfer, or compromised endpoints.
This analytic detects the use of known suspicious SSL certificates by comparing the SHA1 fingerprint from Cisco Secure Firewall logs against a blacklist. This is crucial for identifying malware distribution, C2 infrastructure, or phishing campaigns that reuse or self-sign certificates. Detection of such activity may indicate beaconing, malware download, or data exfiltration over TLS/SSL.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
This analytic identifies Snort intrusion signatures that have been triggered by ten or more distinct internal IP addresses within a one-hour window. It leverages Cisco Secure Firewall Threat Defense logs and focuses on the IntrusionEvent event type to detect activity that may indicate broad targeting or mass exploitation attempts. This behavior is often associated with opportunistic scanning, worm propagation, or automated exploitation of known vulnerabilities across multiple systems. If confirmed malicious, this could represent the early phase of a coordinated attack aiming to gain a foothold on several hosts or move laterally across the environment.
The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
This analytic identifies unexpected Active Directory replication traffic originating from non-domain controller sources. It specifically looks for network traffic associated with AD replication protocols (ms-dc-replication, drsr, ad drs). Such traffic should typically only occur between legitimate domain controllers. Detection of this activity from other sources may indicate malicious activities like DCSync or DCShadow, which are commonly used for credential dumping and could lead to unauthorized access and potential domain-wide compromise.
The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation.
The following analytic identifies potential DNS data exfiltration using a pre-trained deep learning model. It leverages DNS request data from the Network Resolution datamodel and computes features from past events between the same source and domain. The model generates a probability score (pred_is_exfiltration_proba) indicating the likelihood of data exfiltration. This activity is significant as DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising the organization's security posture.
The analytic identifies web activity related to employment searches within a network using Zscaler web proxy logs. It focuses on entries categorized as 'Job/Employment Search' and analyzes data points such as device owner, user, URL category, destination URL, and IP. This detection helps monitor potential insider threats by identifying users who may be seeking new employment, which could indicate a risk of data exfiltration or other insider threats, potentially leading to sensitive information leakage or other security breaches.
The analytic identifies spikes in blocked outbound network connections originating from within an AWS environment. It uses VPC Flow Logs data from CloudWatch, specifically looking for 'blocked' actions from internal IP ranges (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16) to external destinations. A spike is determined by comparing the current number of blocked connections against a calculated baseline (average and standard deviation) over time. This detection is significant as it can indicate potential exfiltration attempts or misconfigurations leading to data leakage. If confirmed malicious, such activity could allow attackers to bypass network defenses, leading to unauthorized data transfer or communication with malicious external entities.
The analytic detects an Azure AD account with concurrent sessions originating from multiple unique IP addresses within a 5-minute window. It leverages Azure Active Directory NonInteractiveUserSignInLogs to identify this behavior by analyzing successful authentication events and counting distinct source IPs. This activity is significant as it may indicate session hijacking, where an attacker uses stolen session cookies to access corporate resources from a different location. If confirmed malicious, this could lead to unauthorized access to sensitive information and potential data breaches.
The following analytic detects suspicious database file and directory discovery activities, which may signal an attacker attempt to locate and assess critical database assets on a compromised system. This behavior is often a precursor to data theft, unauthorized access, or privilege escalation, as attackers seek to identify valuable information stored in databases. By monitoring for unusual or unauthorized attempts to locate database files and directories, this analytic aids in early detection of potential reconnaissance or data breach efforts, enabling security teams to respond swiftly and mitigate the risk of further compromise.
The analytic detects local file inclusion (LFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields such as `request` and `status` to identify suspicious patterns indicative of LFI attempts. This activity is significant because LFI attacks can allow attackers to read sensitive files from the server, potentially exposing critical information. If confirmed malicious, this could lead to unauthorized access to sensitive data, further exploitation, and potential compromise of the Kubernetes environment.
The analytic identifies AWS console login attempts by users from a new region. It leverages AWS CloudTrail events and compares current login regions against a baseline of previously seen regions for each user. This activity is significant as it may indicate unauthorized access attempts or compromised credentials. If confirmed malicious, an attacker could gain unauthorized access to AWS resources, potentially leading to data breaches, resource manipulation, or further lateral movement within the cloud environment.
This analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443) from Cisco Secure Firewall Threat Defense logs. Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious, this indicates potential malware delivery or other nefarious activity.
The analytic detects path traversal command-line execution, often used in malicious documents to execute code via msdt.exe for defense evasion. It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns in process paths. This activity is significant as it can indicate an attempt to bypass security controls and execute unauthorized code. If confirmed malicious, this behavior could lead to code execution, privilege escalation, or persistence within the environment, potentially allowing attackers to deploy malware or leverage other living-off-the-land binaries (LOLBins).
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