FKIE_CVE-2026-48702
Vulnerability from fkie_nvd - Published: 2026-08-13 14:17 - Updated: 2026-08-13 16:18
Severity
Summary
Rekor is a software supply chain transparency log. Starting in version 0.3.0 and prior to version 1.5.2, the `Package.Unmarshal()` function in `pkg/types/alpine/apk.go` decompresses the signature and control gzip members of an APK file into in-memory buffers without bounding the total decompressed size. The existing `max_apk_metadata_size` check (default 1MB) is only applied to individual tar entry header sizes after decompression completes, so it does not prevent a decompression bomb from consuming unbounded heap memory. An attacker can craft a gzip stream that compresses at a ~1000:1 ratio (e.g., 2MB compressed zeros → 2GB decompressed). When submitted as spec.package.content in an Alpine `ProposedEntry`, the server decompresses the full payload into memory during request processing, triggering a fatal Go runtime out-of-memory error or OS OOM-kill that cannot be caught by the server's recover() middleware. This is reachable via two unauthenticated endpoints, `POST /api/v1/log/entries (createLogEntry)` and `POST /api/v1/log/entries/retrieve (searchLogQuery)`. Both invoke `V001Entry.Canonicalize()` → `fetchExternalEntities()` → `apk.Unmarshal(packageData)`, which performs the unbounded decompression. Version 1.5.2 patches the issue. There is no effective workaround. Setting `max_request_body_size` reduces but does not eliminate exposure due to the ~1000:1 compression ratio (a 1MB body limit still allows ~1GB heap allocation). Setting `max_apk_metadata_size` has no effect on this vulnerability since the check is applied after decompression.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "rekor",
"vendor": "sigstore",
"versions": [
{
"status": "affected",
"version": "\u003e= 0.3.0, \u003c 1.5.2"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Rekor is a software supply chain transparency log. Starting in version 0.3.0 and prior to version 1.5.2, the `Package.Unmarshal()` function in `pkg/types/alpine/apk.go` decompresses the signature and control gzip members of an APK file into in-memory buffers without bounding the total decompressed size. The existing `max_apk_metadata_size` check (default 1MB) is only applied to individual tar entry header sizes after decompression completes, so it does not prevent a decompression bomb from consuming unbounded heap memory. An attacker can craft a gzip stream that compresses at a ~1000:1 ratio (e.g., 2MB compressed zeros \u2192 2GB decompressed). When submitted as spec.package.content in an Alpine `ProposedEntry`, the server decompresses the full payload into memory during request processing, triggering a fatal Go runtime out-of-memory error or OS OOM-kill that cannot be caught by the server\u0027s recover() middleware. This is reachable via two unauthenticated endpoints, `POST /api/v1/log/entries (createLogEntry)` and `POST /api/v1/log/entries/retrieve (searchLogQuery)`. Both invoke `V001Entry.Canonicalize()` \u2192 `fetchExternalEntities()` \u2192 `apk.Unmarshal(packageData)`, which performs the unbounded decompression. Version 1.5.2 patches the issue. There is no effective workaround. Setting `max_request_body_size` reduces but does not eliminate exposure due to the ~1000:1 compression ratio (a 1MB body limit still allows ~1GB heap allocation). Setting `max_apk_metadata_size` has no effect on this vulnerability since the check is applied after decompression."
}
],
"id": "CVE-2026-48702",
"lastModified": "2026-08-13T16:18:05.570",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-48702",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-13T15:12:38.632066Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-13T14:17:01.427",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/sigstore/rekor/security/advisories/GHSA-47q9-m4ww-924m"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-770"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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.
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.
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