Raw vector
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-57773 is a high-severity Code Injection (CWE-94) vulnerability in Dataease Dataease. Its CVSS base score is 8.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-57773 is a critical vulnerability in DataEase, an open source business intelligence and data visualization tool. In versions prior to 2.10.12, DB2 parameters are not properly filtered, enabling a JNDI injection attack. This injection triggers an AspectJWeaver deserialization attack, allowing arbitrary file writes. The issue, associated with CWE-94 (code injection) and CWE-502 (deserialization of untrusted data), requires the presence of commons-collections 4.x and aspectjweaver-1.9.22.jar, and carries a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H).
Unauthenticated remote attackers can exploit this vulnerability over the network with low complexity and no user interaction. By injecting malicious JNDI payloads via unfiltered DB2 parameters, attackers trigger deserialization through AspectJWeaver, enabling writes to various files on the target system. This can lead to severe impacts, including high confidentiality, integrity, and availability disruptions, such as remote code execution or system compromise.
The vulnerability has been fixed in DataEase version 2.10.12. Security practitioners should upgrade to this version immediately. Relevant details are available in the project's GitHub security advisory (GHSA-7r8j-6whv-4j5p) and the fixing commit (8d04e92d44e1bac9284e9e64df5afd7f96d9373c).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-25712
Vulnerability Data
DataEase is an open source business intelligence and data visualization tool. Prior to version 2.10.12, because DB2 parameters are not filtered, a JNDI injection attack can be directly launched. JNDI triggers an AspectJWeaver deserialization attack, writing to various files. This…
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vulnerability requires commons-collections 4.x and aspectjweaver-1.9.22.jar. The vulnerability has been fixed in version 2.10.12.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.