CVE-2026-32766
Astral-Tokio-Tar ≤ 0.6.0
Raw vector
CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:U/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-2026-32766 is a low-severity Interpretation Conflict (CWE-436) vulnerability in Astral Astral-Tokio-Tar. Its CVSS base score is 1.7 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 16th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-13414
Vulnerability Data
astral-tokio-tar is a tar archive reading/writing library for async Rust. In versions 0.5.6 and earlier, malformed PAX extensions were silently skipped when parsing tar archives. This silent skipping (rather than rejection) of invalid PAX extensions could be used as a…
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building block for a parser differential, for example by silently skipping a malformed GNU “long link” extension so that a subsequent parser would misinterpret the extension. In practice, exploiting this behavior in astral-tokio-tar requires a secondary misbehaving tar parser, i.e. one that insufficiently validates malformed PAX extensions and interprets them rather than skipping or erroring on them. This vulnerability is considered low-severity as it requires a separate vulnerability against any unrelated tar parser. This issue has been fixed in version 0.6.0.
- CWE(s)
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 cases where two products interpret the same inputs or state transitions differently.
Strict, consistently applied input validation reduces the chance that one product will accept data the other product rejects or interprets differently.
Applying security engineering principles during design can require unambiguous protocol and data-format specifications that eliminate divergent interpretations between products.
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 reduce the chance of introducing parser or state-machine inconsistencies.
Correlating logs from multiple products can surface discrepancies caused by interpretation conflicts.
Runtime monitoring of software behavior can detect adverse outcomes stemming from differing interpretations.
Supplier risk assessments can identify products whose differing interpretations create systemic exposure.
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 can detect and correct cases where one component misinterprets another’s state or messages.
Secure development lifecycle can require consistent interface contracts and canonicalization rules that reduce interpretation conflicts between components.
Explicit application security requirements can mandate unambiguous protocol and data-format specifications that prevent divergent interpretations.
Secure architecture principles include well-defined component boundaries and shared data models that limit conflicting state perceptions.
Secure coding standards can enforce canonical input handling and strict protocol compliance to avoid misinterpretation between products.