CVE-2024-21669 (GCVE-0-2024-21669)
Vulnerability from cvelistv5 – Published: 2024-01-11 05:40 – Updated: 2025-06-03 14:25
VLAI
EPSS
VEX
Title
Hyperledger Aries Cloud Agent Python result of presentation verification not checked for LDP-VC
Summary
Hyperledger Aries Cloud Agent Python (ACA-Py) is a foundation for building decentralized identity applications and services running in non-mobile environments. When verifying W3C Format Verifiable Credentials using JSON-LD with Linked Data Proofs (LDP-VCs), the result of verifying the presentation `document.proof` was not factored into the final `verified` value (`true`/`false`) on the presentation record. The flaw enables holders of W3C Format Verifiable Credentials using JSON-LD with Linked Data Proofs (LDPs) to present incorrectly constructed proofs, and allows malicious verifiers to save and replay a presentation from such holders as their own. This vulnerability has been present since version 0.7.0 and fixed in version 0.10.5.
Severity
9.9 (Critical)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-08 15:36 UTC
CWE
- CWE-347 - Improper Verification of Cryptographic Signature
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/hyperledger/aries-cloudagent-p… | x_refsource_CONFIRM |
| https://github.com/hyperledger/aries-cloudagent-p… | x_refsource_MISC |
| https://github.com/hyperledger/aries-cloudagent-p… | x_refsource_MISC |
| https://github.com/hyperledger/aries-cloudagent-p… | x_refsource_MISC |
| https://github.com/hyperledger/aries-cloudagent-p… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| hyperledger | aries-cloudagent-python |
Affected:
>= 0.7.0, < 0.10.5
Affected: >= 0.11.0rc1, < 0.11.0 |
guessed |
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}
},
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"source": "Red Hat CSAF VEX",
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"title": "Hyperledger Aries Cloud Agent Python result of presentation verification not checked for LDP-VC",
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"vulnrichment": {
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"dateReserved": "2023-12-29T16:10:20.368Z",
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}
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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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