Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
260 detections
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Detects unauthorized modifications to the Palo Alto Networks firewall configuration, potentially indicating malicious activity or security policy violations. These changes can be leveraged to evade detection, facilitate data exfiltration, or disrupt network defenses, ultimately compromising the integrity and security of the environment.
This rule identifies network traffic to known Command and Control (C2) infrastructure associated with the Katz Stealer malware family. It uses a list of hardcoded IP addresses and domains from threat intelligence to detect suspicious outbound connections.
Detects network traffic to or from IP addresses known to be associated with the active exploitation of the SharePoint vulnerability CVE-2025-53770.
Detects high and critical severity correlation events generated by Palo Alto Networks firewalls' automated correlation engine. The correlation engine connects isolated network events and looks for patterns that indicate a more significant event. This helps identify suspicious traffic patterns and network anomalies which, when correlated, indicate with a high probability that a host on the network has been compromised.
Detects when the Palo Alto Networks firewall does not block traffic to a URL associated with malware distribution or operation. This typically indicates a lapse in the firewall's threat intelligence or a misconfiguration. An adversary can abuse this by using the unblocked URL to download malware onto a target system, establish a command and control channel, or exfiltrate data.
Detects when the Palo Alto Networks firewall does not block traffic to a URL known to be used in phishing attacks. An adversary can abuse this by directing victims to the phishing site, potentially stealing credentials, deploying malware, or conducting other malicious activities.
Detects active network communication associated with known malware that is being allowed by the Palo Alto Networks firewall. This may indicate an ongoing security threat, where malicious traffic is bypassing firewall protections, potentially leading to system compromise, data exfiltration, or further infiltration within the network.
Detects medium severity correlation events generated by Palo Alto Networks firewall's automated correlation engine. The correlation engine connects isolated network events and looks for patterns that indicate a more significant event. This helps identify suspicious traffic patterns and network anomalies which, when correlated, indicate with a high probability that a host on the network has been compromised.
Detects allowed network traffic to the TOR network. Adversaries can use TOR to anonymize their network activity, bypass security controls, and evade detection while conducting malicious operations. This could lead to unauthorized access, data exfiltration, and compliance violations if deemed malicious.
KQL Query from file: Detecting Palo Alto Firewall Exploits
KQL Query from file: CVE-2024-3393 DDOS Detection
KQL Query
'The purpose of this content is to identify successful phishing links accessed by users. Once a user clicks on a phishing link, we observe successful network activity originating from non-Microsoft network devices. These devices may include Palo Alto Networks, Fortinet, Check Point, and Zscaler devices.'
This query creates a list of IP addresses with the number of failed login attempts to Entra ID
above a set threshold ( default of 5 ). It then looks for any successful Palo Alto VPN logins from any of these IPs within the same timeframe.
above a set threshold ( default of 5 ). It then looks for any successful Palo Alto VPN logins from any of these IPs within the same timeframe.
'This content is utilized to identify instances of successful login by risky users, who have been observed engaging in potentially suspicious network activity on non-Microsoft network devices.'
'This content is employed to correlate with Microsoft Defender XDR phishing-related alerts. It focuses on instances where a user successfully connects to a phishing URL from a non-Microsoft network device and subsequently makes successful sign-in attempts from the phishing IP address.'
'Identifies anomalous data transfer to public networks. The query leverages built-in KQL anomaly detection algorithms that detects large deviations from a baseline pattern.
A sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
A sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
'Identifies contacts with domains names in CommonSecurityLog that might have been generated by a Domain Generation Algorithm (DGA). DGAs can be used by malware to generate rendezvous points that are difficult to predict in advance.
This detection uses the Alexa Top 1 million domain names to build a model of what normal domains look like. It uses this to identify domains that may have been randomly generated by an algorithm.
The triThreshold is set to 500 - increase this to report on domains that are less likely to have been randomly generated, decrease it for more likely.
The start time and end time look back over 6 hours of data and the dgaLengthThreshold is set to 8 - meaning domains whose length is 8 or more are reported.
NOTE - The top1M csv zip file used in the query is dynamic and may produce different results over various time periods. It's important to cross-check the events against the entities involved in the incident.'
This detection uses the Alexa Top 1 million domain names to build a model of what normal domains look like. It uses this to identify domains that may have been randomly generated by an algorithm.
The triThreshold is set to 500 - increase this to report on domains that are less likely to have been randomly generated, decrease it for more likely.
The start time and end time look back over 6 hours of data and the dgaLengthThreshold is set to 8 - meaning domains whose length is 8 or more are reported.
NOTE - The top1M csv zip file used in the query is dynamic and may produce different results over various time periods. It's important to cross-check the events against the entities involved in the incident.'
'This creates an incident in the event a client generates excessive amounts of DNS queries for non-existent domains.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
'Identifies IP addresses performing DNS lookups associated with common ToR proxies.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
'Identifies anamalous spikes in network traffic logs as compared to baseline or normal historical patterns.
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
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