CVE-2023-32731 (GCVE-0-2023-32731)
Vulnerability from cvelistv5 – Published: 2023-06-09 10:54 – Updated: 2024-09-26 19:12
VLAI
EPSS
VEX
Title
Information leak in gRPC
Summary
When gRPC HTTP2 stack raised a header size exceeded error, it skipped parsing the rest of the HPACK frame. This caused any HPACK table mutations to also be skipped, resulting in a desynchronization of HPACK tables between sender and receiver. If leveraged, say, between a proxy and a backend, this could lead to requests from the proxy being interpreted as containing headers from different proxy clients - leading to an information leak that can be used for privilege escalation or data exfiltration. We recommend upgrading beyond the commit contained in https://github.com/grpc/grpc/pull/33005 https://github.com/grpc/grpc/pull/33005
Severity
7.4 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-26 19:07 UTC
CWE
- CWE-440 - Expected Behavior Violation
Assigner
References
2 references
Impacted products
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Nomenclature
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Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.