Citrix ADC and Gateway CitrixBleed 2 Memory Disclosure
Description
This detection identifies potential exploitation attempts of CVE-2025-5777 (CitrixBleed 2), a memory disclosure vulnerability in Citrix NetScaler ADC and Gateway. The vulnerability is triggered by sending POST requests with incomplete form data to the /p/u/doAuthentication.do endpoint, causing the device to leak memory contents including session tokens and authentication materials. This search looks for POST requests to the vulnerable endpoint that may indicate scanning or exploitation attempts.
Query · spl
| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where
Web.url IN ("*/p/u/doAuthentication.do*")
Web.http_method="POST"
Web.status=200
by Web.http_user_agent, Web.status, Web.http_method,
Web.url, Web.url_length, Web.src, Web.dest, sourcetype
| `drop_dm_object_name("Web")`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `citrix_adc_and_gateway_citrixbleed_2_memory_disclosure_filter`
Implementation guide
To implement this search, ensure that web traffic logs from Citrix NetScaler ADC and Gateway devices are being ingested into Splunk and mapped to the Web data model. The logs should include URL paths, HTTP methods, status codes, source and destination IPs, and user agents. Look specifically for POST requests to /p/u/doAuthentication.do endpoint which is the primary attack vector for CVE-2025-5777.
Known false positives
- Legitimate authentication flows will trigger this detection as they access the doAuthentication.do endpoint. However, repeated automated requests, especially from HeadlessChrome user agents or with incomplete form data, should be investigated. Focus on unusual patterns like multiple rapid requests or non-standard user agents.
Analyst notes
Known false positives: Legitimate authentication flows will trigger this detection as they access the doAuthentication.do endpoint. However, repeated automated requests, especially from HeadlessChrome user agents or with incomplete form data, should be investigated. Focus on unusual patterns like multiple rapid requests or non-standard user agents.