LoFP LoFP / web application

TitleTags
corrupted model files from interrupted downloads, insufficient disk space or memory during legitimate model loading, incompatible model formats or versions, network timeouts when pulling models from registries, file permission issues in multi-user environments, or genuine configuration errors during initial ollama setup may generate similar error patterns during normal operations.
false positives may be present with legitimate applications. attempt to filter by dest ip or use asset groups to restrict to confluence servers.
false positives may vary based on cisco ai defense configuration; monitor and filter out the alerts that are not relevant to your environment.
false positives should be limited, however tune or filter as needed.
legitimate administrative activities such as model inventory management, monitoring dashboards polling model status, automated health checks verifying model availability, ci/cd pipelines validating deployments, development tools inspecting model configurations, or users browsing available models through management interfaces may trigger this detection during normal operations. adjust the threshold based on your environment's baseline activity.
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.
legitimate automated services (ci/cd pipelines, monitoring tools, batch jobs), multiple users behind nat/proxy infrastructure, or authorized load testing activities may trigger this detection during normal operations. operator must adjust threshold accordingly.
legitimate business travelers, remote workers using vpns, users with corporate offices in multiple locations, or employees accessing copilot during international travel may trigger false positives.
legitimate complex queries requiring extensive model reasoning, large context windows processing substantial amounts of text, batch processing operations, or resource-constrained systems experiencing performance degradation may trigger this detection during normal operations.
legitimate employees using personal devices during emergencies, new hires awaiting device provisioning, temporary workers with unmanaged equipment, or users accessing copilot from approved but temporarily non-compliant devices may trigger false positives.
legitimate high-volume production workloads processing multiple concurrent requests, users loading large language models (7b+ parameters) that naturally require substantial memory allocation, simultaneous multi-model deployments during system scaling, batch processing operations, or initial system startup sequences may generate similar memory allocation patterns during normal operations.
legitimate new account creation by authorized administrators will generate similar log entries. however, those should include proper authentication details. verify any detected events against expected administrative activities and authorized user lists.
legitimate remote access from authorized users or applications connecting from non-localhost addresses, temporary network infrastructure issues causing dns resolution failures, firewall or network configuration changes resulting in connection timeouts, cloud-hosted ollama instances receiving valid external api requests, or intermittent connectivity problems during network maintenance may trigger this detection during normal operations.
legitimate researchers studying data classification systems, cybersecurity professionals testing information handling policies, compliance officers reviewing data access procedures, journalists researching transparency issues, or employees asking for comprehensive project documentation may trigger false positives.
legitimate users discussing ai ethics research, security professionals testing system robustness, developers creating training materials for ai safety, or academic discussions about ai limitations and behavioral constraints may trigger false positives.
legitimate users experiencing network connectivity issues, traveling employees with intermittent vpn connections, users in regions with unstable internet infrastructure, or password reset activities during business travel may trigger false positives.
legitimate web application clients or mobile apps that access multiple api endpoints as part of normal functionality, monitoring and health check systems probing various endpoints for availability, load balancers performing health checks across different paths, api testing frameworks during development and qa processes, or users navigating through web interfaces that trigger multiple api calls may generate similar patterns during normal operations.
limited false positives should occur as this pattern is highly specific to cve-2025-24813 exploitation. however, legitimate application errors that use similar cookie patterns and result in 500 status codes might trigger false positives. review the jsessionid cookie format and the associated request context to confirm exploitation attempts.
no known false positives for this detection. if the alerts are noisy, consider tuning this detection by using the _filter macro in this search, and/or updating the tool this alert originates from.
normal service restarts during system updates or maintenance windows, graceful shutdowns with non-zero exit codes, intentional service stops by administrators, software upgrades requiring process termination, out-of-memory conditions on resource-constrained systems, or known bugs in specific ollama versions that cause benign crashes may trigger this detection during routine operations.
power users, executives with heavy ai workloads, employees traveling for business, users accessing multiple copilot applications legitimately, or teams using shared corporate accounts across different office locations may trigger false positives.
some legitimate applications might use put requests to create .session files, especially in custom implementations that leverage tomcat's session persistence mechanism. verify if the detected activity is part of a normal application flow or if it correlates with other suspicious behavior, such as subsequent get requests with manipulated jsessionid cookies.