Raw vector
CVSS:4.0/AV:N/AC:H/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/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:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-2555 is a low-severity Improper Input Validation (CWE-20) vulnerability in Jeecg Jeecg Boot. Its CVSS base score is 2.3 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked in the top 49% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other Platforms; in the Other ATLAS/OWASP Terms risk domain.
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-2026-2555 is a deserialization vulnerability in JeecgBoot version 3.9.1, affecting the importDocumentFromZip function within the org/jeecg/modules/airag/llm/controller/AiragKnowledgeController.java file of the Retrieval-Augmented Generation (RAG) component. The issue stems from improper input validation (CWE-20) and deserialization of untrusted data (CWE-502), with a CVSS v3.1 base score of 5.0 (AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:L/A:L). It was published on 2026-02-16.
The vulnerability can be exploited remotely by an authenticated attacker with low privileges (PR:L), though it requires high attack complexity (AC:H) and no user interaction (UI:N). Successful exploitation leads to limited impacts on confidentiality, integrity, and availability (C:L/I:L/A:L), potentially allowing arbitrary code execution or data manipulation via malicious ZIP files processed by the import function.
Advisories from VulDB indicate the project was notified early through GitHub issue #9335 in the JeecgBoot repository, but the maintainers have not yet responded or released patches. No specific mitigations are detailed in the available references.
This vulnerability is notable for its presence in a Retrieval-Augmented Generation component, which has relevance to AI/ML workflows, and its exploitability is described as difficult due to the high complexity required. No real-world exploitation has been reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-6091
Vulnerability Data
A weakness has been identified in JeecgBoot 3.9.1. This vulnerability affects the function importDocumentFromZip of the file org/jeecg/modules/airag/llm/controller/AiragKnowledgeController.java of the component Retrieval-Augmented Generation. Executing a manipulation can lead to deserialization. The attack can be launched remotely. Attacks of this nature…
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are highly complex. It is stated that the exploitability is difficult. The project was informed of the problem early through an issue report but has not responded yet.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing input validation through analysis or test cases.
SI-10 directly requires validity checks on information inputs, structurally preventing improper or missing validation.
Requiring documented development standards and tools can embed input-validation practices into the engineering process.
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 (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.
Testing against a defined set of requirements and using code review plus vulnerability scanning forces validation of inputs and handling of unanticipated conditions, reducing the chance that malformed data will be accepted.
Requiring language-specific secure coding standards, peer review, SAST and documented mitigation of common programming errors forces validation of all inputs before they are trusted.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Hardening callouts derived
DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.
- No fine-grained STIG row for CWE-20, a class-level weakness, and none of its children carries one either.