CVE-2025-61301 identifies a denial-of-analysis vulnerability in CAPEv2, an open-source malware sandbox widely used for behavioral analysis. The flaw resides in the reporting components, specifically reporting/mongodb.py and reporting/jsondump.py, which process behavioral data generated during sample execution. Attackers who can submit samples to the sandbox can craft inputs that produce deeply nested or oversized behavior data structures. These inputs trigger MongoDB BSON document size limits or cause recursion errors in the orjson library used for JSON serialization. When these limits are exceeded, the reporting modules fail to generate complete behavioral analysis reports, resulting in incomplete or missing output. This effectively denies analysts the ability to fully understand the behavior of submitted samples, undermining the sandbox's primary function. The vulnerability does not require authentication beyond sample submission, making it accessible to any user able to submit files to the sandbox. No patches or fixes are currently linked, and no exploits have been observed in the wild. The issue highlights the need for robust input validation and error handling in sandbox reporting pipelines to prevent denial-of-service conditions caused by malformed or maliciously crafted samples.

For European organizations, especially those relying on CAPEv2 for malware analysis and threat intelligence, this vulnerability can significantly degrade the quality and completeness of behavioral reports. Incomplete analysis hampers incident response teams' ability to detect, understand, and mitigate malware threats effectively. This can lead to delayed detection of advanced persistent threats or zero-day malware, increasing the risk of data breaches or operational disruptions. Organizations involved in cybersecurity research, digital forensics, and threat hunting that utilize CAPEv2 may find their workflows disrupted. The impact is primarily on the availability and reliability of analysis data, which indirectly affects confidentiality and integrity by impairing timely threat mitigation. Given the open-source nature of CAPEv2 and its adoption in various European cybersecurity communities, the scope of affected systems could be broad within these sectors.

To mitigate CVE-2025-61301, organizations should implement strict input validation on behavioral data generated by sandboxed samples to prevent deeply nested or oversized structures from reaching the reporting modules. Limiting the maximum size and depth of behavior data before serialization can prevent triggering BSON limits or recursion errors. Enhancing error handling in reporting/mongodb.py and reporting/jsondump.py to gracefully handle serialization failures and provide fallback mechanisms will reduce report loss. Sandbox administrators should monitor for unusually large or complex sample behaviors and consider sandbox environment restrictions to limit resource consumption. Applying patches or updates from CAPEv2 maintainers once available is critical. Additionally, segregating the analysis environment and implementing alerting for failed report generations can help detect exploitation attempts early. Finally, educating users submitting samples about acceptable input parameters may reduce accidental triggering of this vulnerability.