CVE-2023-30610 (GCVE-0-2023-30610)
Vulnerability from cvelistv5 – Published: 2023-04-19 17:18 – Updated: 2025-02-05 14:56
VLAI
EPSS
VEX
Title
AWS SDK for Rust will log AWS credentials when TRACE-level logging is enabled for request sending
Summary
aws-sigv4 is a rust library for low level request signing in the aws cloud platform. The `aws_sigv4::SigningParams` struct had a derived `Debug` implementation. When debug-formatted, it would include a user's AWS access key, AWS secret key, and security token in plaintext. When TRACE-level logging is enabled for an SDK, `SigningParams` is printed, thereby revealing those credentials to anyone with access to logs. All users of the AWS SDK for Rust who enabled TRACE-level logging, either globally (e.g. `RUST_LOG=trace`), or for the `aws-sigv4` crate specifically are affected. This issue has been addressed in a set of new releases. Users are advised to upgrade. Users unable to upgrade should disable TRACE-level logging for AWS Rust SDK crates.
Severity
5.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-05 14:55 UTC
CWE
- CWE-532 - Insertion of Sensitive Information into Log File
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/awslabs/aws-sdk-rust/security/… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| awslabs | aws-sdk-rust |
Affected:
>= 0.2.0, < 0.2.1
Affected: >= 0.3.0, < 0.3.1 Affected: >= 0.4.1, < 0.4.2 Affected: >= 0.5.2, < 0.5.3 Affected: >= 0.6.0, < 0.6.1 Affected: >= 0.7.0, < 0.7.1 Affected: >= 0.8.0, < 0.8.1 Affected: >= 0.9.0, < 0.9.1 Affected: >= 0.10.1, < 0.10.2 Affected: >= 0.11.0, < 0.11.1 Affected: >= 0.12.0, < 0.12.1 Affected: >= 0.13.0, < 0.13.1 Affected: >= 0.14.0, < 0.14.1 Affected: >= 0.15.0, < 0.15.1 Affected: >= 0.46.0, < 0.46.1 Affected: >= 0.47.0, < 0.47.1 Affected: >= 0.48.0, < 0.48.1 Affected: >= 0.49.0, < 0.49.1 Affected: >= 0.50.0, < 0.51.1 Affected: >= 0.52.0, < 0.52.1, Affected: >= 0.53.1, < 0.53.2 Affected: >= 0.54.1, < 0.54.2 Affected: >= 0.55.0, < 0.55.1 |
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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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.
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