CVE intelligence and bounded remediation
CVE-2026-34445 — Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python’s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX model file. It didn’t check if the "keys" in the file were valid. Due to this, an attacker could craft a malicious model that overwrites internal object properties. This issue has been patched in version 1.21.0.
- Severity
- High
- CVSS
- 8.6 (3.1)
- Published
- 2026-04-01
- CISA KEV
- Not currently listed
- Ecosystem
- python/pypi
- Weaknesses
- CWE-20, CWE-400, CWE-915
Affected products
- linuxfoundation / onnx
Matched remediation archetype
Resource exhaustion and denial of service
This catalog composition supplies bounded fallback guidance. Explicitly reviewed curated workflows load with the complete record below.
Check exposure
- Identify attacker-influenced work factors including input size, nesting, compression, fan-out, regex cost, allocation, recursion, retries, and connection lifetime.
- Map per-request and shared CPU, memory, disk, descriptor, thread, queue, and downstream-service limits.
- Determine whether authentication, tenancy, quotas, and rate controls apply before expensive processing begins.
Remediate safely
- Bound input size, nesting, expansion, work, concurrency, queue depth, retries, and execution time before resource-intensive processing.
- Release resources on every success, error, cancellation, and timeout path and use backpressure instead of unbounded buffering.
- Update affected components and add small deterministic tests that assert resource ceilings rather than exhausting a host.
Authoritative sources
Complete CVE record and remediation plan
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