CVE-2026-45676
Opentelemetry Ebpf Instrumentation ≤ 0.9.0
Raw vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-45676 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Opentelemetry Ebpf Instrumentation. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Application or System Exploitation (T1499.004); ranked at the 6th 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 SI-10 (Information Input Validation) and SI-11 (Error Handling) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-33951
Vulnerability Data
OpenTelemetry eBPF Instrumentation provides eBPF instrumentation based on the OpenTelemetry standard. Prior to version 0.9.0, OBI's replacement ELF parser trusts section offsets, counts, and string offsets from the executable file. A crafted local ELF can make OBI dereference invalid section…
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pointers or slice past string tables, causing the agent to panic while determining the process language. This issue has been patched in version 0.9.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CWE-20/248 in local ELF parser allows crafted file to trigger agent panic (DoS via application exploitation); direct mapping to T1499.004 with uncertainty on attacker-controlled ELF delivery path.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces validation of untrusted ELF section/string offsets before use, eliminating the root cause of invalid pointer/slice operations.
Requires graceful error handling for malformed input so that parsing failures produce controlled errors instead of language-level panics.
Mandates fail-safe behavior that keeps the agent operational when language or ELF parsing encounters unexpected data.
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 and enforce input validation during development.
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.
Testing against a defined set of requirements and using code review plus vulnerability scanning forces validation of inputs and handling of unanticipated conditions, reducing the chance that malformed data will be accepted.
Secure-coding guidelines and mandatory security testing (including code scans) compel developers to validate and sanitize inputs at design and implementation time, lowering the incidence of malformed or malicious data reaching downstream components.
Mandating input controls that include integrity checks and input validation ensures that untrusted data is examined before use, blocking the root cause of many injection and malformed-data weaknesses.
Security-by-design principles explicitly call for data validation and sanitization at every layer, reducing the chance that malformed or malicious input will be processed without scrutiny.
Requiring language-specific secure coding standards, peer review, SAST and documented mitigation of common programming errors forces validation of all inputs before they are trusted.
Regular automated validation of system software and data content, combined with scanning of all inbound files, enforces input validation at the boundary before untrusted content is processed.