CVE-2025-61301 in CAPEv2
Summary
by MITRE • 10/21/2025
Denial-of-analysis in reporting/mongodb.py and reporting/jsondump.py in CAPEv2 (commit 52e4b43, on 2025-05-17) allows attackers who can submit samples to cause incomplete or missing behavioral analysis reports by generating deeply nested or oversized behavior data that trigger MongoDB BSON limits or orjson recursion errors when the sample executes in the sandbox.
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Analysis
by VulDB Data Team • 10/21/2025
The vulnerability CVE-2025-61301 represents a critical denial-of-analysis flaw within the CAPEv2 automated malware analysis platform, specifically targeting the reporting module components in reporting/mongodb.py and reporting/jsondump.py. This issue arises from insufficient input validation and handling of behavioral analysis data generated during malware sandbox execution, creating a scenario where malicious actors can deliberately craft samples designed to disrupt the analysis process. The vulnerability manifests when attackers submit specially crafted samples that generate deeply nested or oversized behavioral data structures, which then trigger system limitations during the reporting phase.
The technical exploitation of this vulnerability leverages the inherent constraints of MongoDB's BSON data format and the orjson serialization library. When behavioral analysis data exceeds MongoDB's BSON document size limits or when orjson encounters excessive recursion depths during JSON serialization, the system fails to produce complete analysis reports. This occurs because the reporting module lacks proper error handling and data sanitization mechanisms to manage pathological data structures that can emerge from malicious sample execution. The flaw operates at the intersection of data processing and storage limitations, where the legitimate analysis output becomes corrupted or truncated due to system-imposed constraints rather than actual analysis failures.
From an operational impact perspective, this vulnerability severely compromises the integrity and completeness of malware analysis workflows within CAPEv2 environments. Security analysts relying on these platforms for threat intelligence and behavioral analysis may receive incomplete or missing reports, leading to potential gaps in threat detection and response capabilities. The vulnerability affects the core analysis pipeline where behavioral data is collected, processed, and reported, potentially allowing malicious samples to evade detection by simply triggering the denial-of-analysis condition. This creates a scenario where the very system designed to identify and analyze threats becomes vulnerable to being disrupted by the threats themselves, undermining the fundamental premise of automated malware analysis.
The vulnerability aligns with CWE-400 (Uncontrolled Resource Consumption) and CWE-121 (Stack-based Buffer Overflow) categories, reflecting both resource exhaustion and data processing errors in the analysis pipeline. From an ATT&CK framework perspective, this vulnerability maps to T1588.002 (Tool Development) and T1497.001 (Virtualization/Sandbox Evasion) as attackers can develop samples specifically designed to exploit these weaknesses. The flaw demonstrates a classic case of input manipulation leading to system-level failure, where the analysis platform's defensive mechanisms are bypassed through the strategic generation of problematic data structures that exploit implementation-specific limitations rather than fundamental security flaws.
Mitigation strategies should focus on implementing robust input validation and sanitization within the reporting modules, establishing proper error handling for BSON size limits and JSON serialization recursion, and introducing data size monitoring and enforcement mechanisms. Organizations should consider implementing rate limiting and data size restrictions on behavioral analysis outputs, while also ensuring that the MongoDB and orjson components are properly configured to handle edge cases gracefully. Regular security assessments of the analysis platform's data processing pipelines should include testing for pathological input conditions to identify similar vulnerabilities before they can be exploited by malicious actors.