CVE-2026-32240
Capnproto ≤ 1.4.0
Raw vector
CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:L/VI:L/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-2026-32240 is a medium-severity Numeric Truncation Error (CWE-197) vulnerability in Capnproto Capnproto. Its CVSS base score is 6.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 11th 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 SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-11688
Vulnerability Data
Cap'n Proto is a data interchange format and capability-based RPC system. Prior to 1.4.0, when using Transfer-Encoding: chunked, if a chunk's size parsed to a value of 2^64 or larger, it would be truncated to a 64-bit integer. In theory,…
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this bug could enable HTTP request/response smuggling. This vulnerability is fixed in 1.4.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V4.1.3V4.2.4V1.5.3V4.1.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover truncation errors through static analysis, dynamic testing, or code review.
Documented development standards and tools can mandate use of safe arithmetic libraries or explicit checks against truncation.
Engineering principles can require safe type conversions and avoidance of narrowing casts that cause truncation.
Boundary protection at external interfaces can enforce consistent HTTP request/response parsing rules between intermediaries and endpoints.
Validating HTTP inputs at the intermediary prevents malformed messages from being interpreted inconsistently downstream.
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.
Configuration management can enforce uniform HTTP parsing rules across intermediaries, directly mitigating inconsistent interpretation.
Secure SDLC practices directly prevent truncation errors via reviews, static analysis, and safe type handling.
Network monitoring can detect smuggling attempts via anomalous HTTP traffic or logs, while eliminating the inconsistency directly aids detection of such events.
Network protections can enforce consistent HTTP proxy/firewall behavior to block smuggling, and removing the weakness helps prevent unauthorized access via request smuggling.
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 truncation bugs through static analysis and fuzzing.
Network security controls can enforce consistent HTTP parsing and proxy behavior that mitigates request smuggling.
Secure network services include hardening proxies and gateways against inconsistent HTTP interpretation.
Secure development lifecycle mandates practices that can catch numeric truncation during design and code review.
Application security requirements can specify safe numeric handling and data-type constraints.
Secure architecture principles include choosing appropriate data types and avoiding unsafe casts.