CVE-2026-53655
Isaacs Tar ≤ 7.5.16
Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/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-2026-53655 is a medium-severity Interpretation Conflict (CWE-436) vulnerability in Isaacs Tar. 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 4th 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-38255
Vulnerability Data
node-tar is a full-featured Tar for Node.js. Prior to 7.5.16, tar (node-tar) applies a PAX extended header's size= record (and other PAX overrides) to the next header entry of any type, including intermediary metadata headers such as a GNU long-name…
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(L) or long-link (K) entry. Per POSIX pax, a PAX extended header (x) describes the next file entry, not the intermediary extension headers that may sit between the x header and the file it annotates. Because node-tar lets the PAX size override the byte length of an intervening L/K/x header, an attacker can desynchronize node-tar's stream cursor relative to every other mainstream tar implementation (GNU tar, libarchive/bsdtar, Python tarfile, and the now-fixed tar-rs / astral-tokio-tar). The result is a tar parser interpretation differential (CWE-436): a single crafted archive yields a different set of members under node-tar than under the reference tar tools. An attacker can use this to hide a member from one parser while it is visible to another, which defeats security tooling whose scanner and extractor disagree on archive contents (e.g. a malware/secret scanner that lists entries with one library while a downstream step extracts with another) This vulnerability is fixed in 7.5.16.
- 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.