CVE-2026-4998
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/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-2026-4998 is a medium-severity Injection (CWE-74) vulnerability. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 42th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the LLM/Generative AI Risks 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-2026-4998 is a code injection vulnerability in Sinaptik AI PandasAI versions up to 3.0.0. The issue resides in the CodeExecutor.execute function within the file pandasai/core/code_execution/code_executor.py, part of the Chat Message Handler component. It allows manipulation that leads to arbitrary code execution, as identified by CWEs-74 (Improper Neutralization of Special Elements in Output Used by a Downstream Component) and CWE-94 (Improper Control of Generation of Code). The vulnerability carries a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L), indicating high severity due to its network-accessible nature with low attack complexity and no required privileges.
Attackers can exploit this vulnerability remotely by crafting malicious input to the affected Chat Message Handler, leading to code injection without authentication. Successful exploitation grants limited impacts on confidentiality, integrity, and availability, potentially allowing attackers to execute arbitrary code in the context of the PandasAI application. No user interaction is needed, making it suitable for automated attacks against exposed instances.
Advisories from VulDB, including entries at vuldb.com/vuln/353885 and vuldb.com/vuln/353885/cti, detail the issue but note no vendor response despite early contact. A public exploit is available at gist.github.com/YLChen-007/78ed1dbcccdb8895adb230dddde3316d, increasing the risk of active attacks. No patches or mitigations are provided by the vendor Sinaptik AI.
This vulnerability is particularly relevant to AI/ML workflows, as PandasAI integrates conversational AI capabilities with pandas dataframes, potentially exposing data processing pipelines in automated analysis environments to remote code execution risks. The public exploit availability heightens the urgency for users to isolate or upgrade affected deployments.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16925
Vulnerability Data
A weakness has been identified in Sinaptik AI PandasAI up to 3.0.0. This vulnerability affects the function CodeExecutor.execute of the file pandasai/core/code_execution/code_executor.py of the component Chat Message Handler. Executing a manipulation can lead to code injection. The attack may be…
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launched remotely. The exploit has been made available to the public and could be used for attacks. The vendor was contacted early about this disclosure but did not respond in any way.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1426 — Model-generated code reaches exec sink with no validation (CWE-1426).
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.1V1.2.3V1.2.5V1.2.8
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.
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.
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.
Secure SDLC practices directly require input validation and output encoding that prevent injection flaws.
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.
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 in development catches injection vulnerabilities before release.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection attacks in software design.
Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.