Raw vector
CVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/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-2025-32798 is a high-severity Code Injection (CWE-94) vulnerability in Anaconda Conda-Build. Its CVSS base score is 8.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 50th percentile by exploit likelihood (below the median); 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.
Conda-build, a tool for building conda packages, is affected by CVE-2025-32798 prior to version 25.4.0. The vulnerability arises in the recipe processing logic that handles selectors embedded in meta.yaml files. It relies on Python's eval function to interpret these user-supplied expressions without sanitization, enabling arbitrary code execution during builds and violating the assumption that recipe content is trusted.
An attacker who can supply or influence a malicious meta.yaml file can trigger code execution in the build environment. Successful exploitation grants control over commands and file operations on the host, compromising confidentiality, integrity, and availability of the build process. The issue maps to CWE-94 and carries a CVSS 4.0 score of 8.2 with network attack vector and high impact metrics.
The official GitHub Security Advisory GHSA-6cc8-c3c9-3rgr and the associated commit document that the flaw was addressed by replacing unsafe eval usage with a safer selector parser in version 25.4.0. Users are advised to upgrade conda-build to the patched release to eliminate the execution pathway.
EPSS remains low and unchanged at 0.0121 with no observed rise after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-18460
Vulnerability Data
Conda-build contains commands and tools to build conda packages. Prior to version 25.4.0, the conda-build recipe processing logic has been found to be vulnerable to arbitrary code execution due to unsafe evaluation of recipe selectors. Currently, conda-build uses the eval…
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function to process embedded selectors in meta.yaml files. This approach evaluates user-defined expressions without proper sanitization, which allows arbitrary code to be executed during the build process. As a result, the integrity of the build environment is compromised, and unauthorized commands or file operations may be performed. The vulnerability stems from the inherent risk of using eval() on untrusted input in a context intended to control dynamic build configurations. By directly interpreting selector expressions, conda-build creates a potential execution pathway for malicious code, violating security assumptions. This issue has been patched in version 25.4.0.
- 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.
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.
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.
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.
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.