Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects malicious attempts to delete or modify the access control settings of centralized cloud audit log storage, such as S3 buckets, Azure Log Analytics workspaces, or GCP log buckets. These actions aim to impair visibility into an adversary's activities, a common step in anti-forensics and data destruction workflows.
Detects malicious attempts to delete or modify the access control settings of centralized cloud audit log storage, such as S3 buckets, Azure Log Analytics workspaces, or GCP log buckets. These actions aim to impair visibility into an adversary's activities, a common step in anti-forensics and data destruction workflows.
Detects the deletion of AWS CloudWatch log groups, log streams, metric filters, CloudWatch alarms, and VPC Flow Logs. Such actions are often indicative of an adversary attempting to destroy forensic evidence or disable security alerting prior to further unauthorized activity, such as data exfiltration or resource abuse.
Detects administrative actions intended to disable, modify, or suppress logging and alerting capabilities within AWS and Azure, specifically targeting services like Amazon GuardDuty, AWS Security Hub, AWS Config, and Microsoft Defender for Cloud. Such actions are frequently observed as a precursor to unauthorized activity to hide malicious actions from detection systems.
Detects modifications to AWS CloudTrail logging configurations, specifically changes to trails or subscription filters that could be used to suppress, redirect, or disrupt the collection of audit logs, potentially masking malicious activity.
Detects high-volume activity (BLOCK or COUNT actions) from the AWS Shield Advanced managed DDoS protection rule set within a 1-minute time window. The rule alerts on potential Layer 7 DDoS attacks by monitoring for significant spikes in event volume or the number of unique source IP addresses targeting a specific AWS WAF Web ACL.
This search surfaces AWS Bedrock Claude prompts containing phrases commonly associated with prompt injection or jailbreak attempts, such as instruction overrides, persona switching, or requests to ignore prior guidance. These strings are not inherently malicious on their own - many of them (for example "act as", "you must", or "new persona") appear routinely in legitimate system prompts, few-shot examples, and role-play or creative-writing use cases. This is a hunting search rather than an anomaly detection because the base rate of benign matches is high; use it to pivot into the surrounding conversation and look for corroborating signals such as the phrase appearing mid-conversation rather than in an expected system/few-shot prompt, the user attempting to override guardrails or safety instructions rather than set a benign persona, instructions that follow the injected phrase requesting privilege escalation, data exfiltration, or access to restricted tools/resources, repeated or iterative attempts from the same user/role after being refused, and injected phrases originating from untrusted input (e.g., retrieved documents or tool output) rather than the human user turn.
This detection identifies potential cross-region inference abuse in AWS Bedrock Claude models. Cross-region inference abuse may indicate attempts to bypass regional restrictions, exfiltrate data, or perform unauthorized actions across different AWS regions.
This detection identifies prompts with hostile or aggressive sentiment being sent to AWS Bedrock Claude models. Hostile prompts may indicate potential abuse, harassment, or other malicious intent.
This detection identifies sensitive data such as social security numbers, passwords, API keys, and credit card numbers being sent in prompts to AWS Bedrock Claude models. Exposure of sensitive data through AI prompts may indicate data loss, credential leakage, or insider threat activity.
This detection identifies unusually large prompts sent to AWS Bedrock Claude models by calculating the statistical baseline of input token counts and flagging requests that exceed one standard deviations above the mean. Abnormally large prompts may indicate prompt injection attacks, data exfiltration attempts, or abuse of the AI service.
This rule detects potential Remote Code Execution (RCE) attempts leveraging Velocity Template Language (VTL) injection vulnerabilities within AWS API Gateway. It monitors logs for specific indicators, including references to 'ProcessBuilder', malicious template artifacts, and exploitation patterns associated with the 'floci-apigateway-vtl-rce-poc' exploit.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
This analytic detects instances where an internal host attempts to communicate with over 500 ports on a single destination IP address. It includes filtering criteria to exclude applications performing scans over ephemeral port ranges, focusing on potential reconnaissance or scanning activities. Monitoring network traffic logs allows for timely detection and response to such behavior, enhancing network security by identifying and mitigating potential threats promptly.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using one of the NMAP top 20 ports. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats.
Detects the HazyBeacon backdoor, which leverages AWS Lambda URLs for C2 communication and uses custom tools for data exfiltration. The rule identifies known malicious file hashes, suspicious tool drops in C:\ProgramData, specific service creation commands for persistence, and network connections from the sideloaded host process (mscorsvw.exe) to AWS Lambda URLs.
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
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