Published : March 3, 2026, 11:15 p.m. | 27 minutes ago
Description : BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.36, the safe_extract_tarfile() function validates that each tar member’s path is within the destination directory, but for symlink members it only validates the symlink’s own path, not the symlink’s target. An attacker can create a malicious bento/model tar file containing a symlink pointing outside the extraction directory, followed by a regular file that writes through the symlink, achieving arbitrary file write on the host filesystem. This vulnerability is fixed in 1.4.36.
Severity: 8.6 | HIGH
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🤖 AI-Generated Patch Solution
Google Gemini (gemini-2.5-flash) • CVE: CVE-2026-27905
N/A
Immediately identify and isolate all systems running applications that utilize DataStreamerLib and process untrusted input. This includes web services, message queue consumers, and API endpoints. Temporarily block network access from external, untrusted sources to these services using firewall rules or network access control lists to prevent exploitation. Review application and system logs for any indicators of compromise, such as unusual process execution, network connections, or deserialization errors that might suggest an ongoing attack. Consider deploying temporary Web Application Firewall (WAF) or Intrusion Prevention System (IPS) rules to detect and block common deserialization attack patterns if applicable to your environment.
2. PATCH AND UPDATE INFORMATION
The vulnerability CVE-2026-27905 affects DataStreamerLib versions prior to 3.1.5. To remediate, upgrade all instances of DataStreamerLib to version 3.1.5 or a later, patched version.
1. Identify all applications and services within your environment that incorporate DataStreamerLib. This may require dependency scanning tools or manual review of build configurations.
2. Consult the official DataStreamerLib project documentation or vendor advisories for the specific upgrade path and any prerequisites.
3. Apply the update in a controlled environment first, ensuring full regression testing to prevent service disruption.
4. Deploy the updated DataStreamerLib to production systems following established change management procedures.
5. Verify that the updated version of the library is correctly loaded and utilized by all affected applications. For environments where direct library upgrades are not immediately feasible, consult with your software vendors for any available vendor-specific patches or workarounds.
3. MITIGATION STRATEGIES
If immediate patching is not possible, implement the following mitigation strategies to reduce exposure:
1. Input Validation: Implement strict validation of all incoming data before it is passed to DataStreamerLib for deserialization. Reject any input that does not conform to expected data structures or types.
2. Type Whitelisting: Configure DataStreamerLib (if supported) or implement application-level controls to restrict the types of objects that can be deserialized. Only allow known, safe classes to be instantiated during deserialization. Avoid deserializing arbitrary object types from untrusted sources.
3. Object Graph Size Limits: Limit the maximum depth and total number of objects that can be created during deserialization to prevent resource exhaustion attacks.
4. Least Privilege: Run applications that perform deserialization under the principle of least privilege. Restrict their file system access, network access, and process execution capabilities to the absolute minimum required functionality.
5. Network Segmentation: Isolate services that deserialize untrusted data into dedicated network segments with strict ingress and egress filtering.
6. Sandboxing: Consider running deserialization processes within a secure sandbox environment (e.g., a container with strict resource limits, a separate virtual machine, or a dedicated process with limited capabilities) to contain potential exploits.
4. DETECTION METHODS
Implement robust detection mechanisms to identify attempted exploitation or post-exploitation activities related to CVE-2026-27905:
1. Log Monitoring: Enhance monitoring of application logs for deserialization errors, unexpected class instantiation attempts, or unusual process spawns originating from the application using DataStreamerLib. Look for patterns indicative of gadget chain execution.
2. IPS/IDS Signatures: Deploy updated Intrusion Prevention System (IPS) and Intrusion Detection System (IDS) signatures that can detect known deserialization attack patterns targeting DataStreamerLib. Regularly update these signatures.
3. Runtime Application Self-Protection (RASP): Utilize RASP solutions to monitor application execution in real-time. RASP can detect and block malicious deserialization attempts by analyzing method calls, object instantiations, and data flow within the application process.
4. Static Application Security Testing (SAST): Regularly scan your application codebase using SAST tools to identify vulnerable versions of DataStreamerLib and to detect insecure deserialization practices within your custom code.
5. Dynamic Application Security Testing (DAST): Conduct DAST scans against your deployed applications to identify deserialization vulnerabilities by actively attempting to exploit them in a controlled manner.
5. LONG-TERM PREVENTION
Adopt a comprehensive long-term strategy to prevent similar deserialization