RHSA-2026:70996
Vulnerability from csaf_redhat - Published: 2026-09-23 17:13 - Updated: 2026-09-24 06:05Summary
Red Hat Security Advisory: Red Hat AI Inference Model Optimization Tools 3.4.5 (cuda)
Severity
Important
Notes
Topic: Red Hat AI Inference Model Optimization Tools 3.4.5 (cuda) is now available.
Details: Red Hat AI Inference Model Optimization Tools
Terms of Use: This content is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). If you distribute this content, or a modified version of it, you must provide attribution to Red Hat Inc. and provide a link to the original.
A flaw was found in python-transformers. An attacker can exploit this vulnerability by providing a malicious model repository. During model initialization, the `trust_remote_code` parameter, intended to prevent remote code execution, is overridden by untrusted configuration data. This allows the attacker to execute arbitrary code, potentially leading to credential theft, unauthorized access, or the deployment of backdoors.
7.7 (High)
Affected products
Fixed
2 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64 | — |
Vendor Fix
fix
Workaround
|
Threats
Impact
Important
A flaw was found in Pillow prior to 12.3.0. When an uncompressed McIdas AREA image is loaded from a filename through the mmap raw codec path, attacker-controlled header words can set a row stride smaller than the natural row width. Pixel access such as Image.tobytes(), getpixel, convert, or save can then read beyond the mapped region, disclosing adjacent process memory or causing a fault.
9.1 (Critical)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. When processing a specially crafted font file, the library's font compilation function does not adequately check for excessive memory allocation. This oversight allows a remote attacker to trigger an unreasonable consumption of system memory, leading to a denial of service (DoS) for the application.
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. This vulnerability allows a remote attacker to cause a Denial of Service (DoS) by providing a specially crafted BDF font file. The library's image processing function fails to properly validate dimensions from the font file, bypassing a critical security check designed to prevent excessive memory usage. This oversight can lead to the system consuming an unreasonable amount of memory, making it unavailable to legitimate users.
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted GD 2.x image file. The GdImageFile._open() function reads image dimensions without proper validation, leading to excessive memory allocation. This can result in a Denial of Service (DoS) due to C-heap exhaustion.
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow prior to 12.3.0. The public RankFilter API can trigger a native heap out-of-bounds write when given a very large odd filter size. ImageFilter.RankFilter.filter() calls image.expand(size // 2, size // 2) before rank-filter size validation, and ImagingExpand() computes output dimensions with unchecked signed integer arithmetic. This can lead to denial of service and limited integrity impact via heap corruption.
8.2 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the library's image processing functions, specifically when handling image coordinates near certain limits. This flaw, a native heap out-of-bounds write, could lead to a denial of service (DoS), making the affected system or application unavailable.
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit a vulnerability in the PdfParser.PdfStream.decode() function when processing a crafted FlateDecode PDF stream. By providing a specially designed PDF file, the attacker could cause the application to exhaust available memory, leading to a denial of service (DoS).
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted JPEG2000 image file. Due to incorrect calculation of memory requirements for image tiles, processing this file can lead to excessive memory consumption, resulting in a denial of service (DoS) through out-of-memory failures.
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. This vulnerability allows an attacker to trigger controlled native heap corruption by supplying an output image whose mode does not match the transform's declared output mode when using the ImageCms.ImageCmsTransform.apply API. This can lead to a denial of service (DoS) condition, making the affected system or application unavailable.
7.5 (High)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in AnyIO. Untrusted or faulty worker code can write a large amount of data to the standard error (stderr) stream. This data fills a pipe that the parent process fails to drain, causing the process-pool call to block indefinitely. This can lead to a Denial of Service (DoS) condition, making the application unresponsive.
5.5 (Medium)
Affected products
Fixed
2 products, the same list as for
CVE-2026-5241
Threats
Impact
Moderate
References
90 references
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
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"cwe": {
"id": "CWE-125",
"name": "Out-of-bounds Read"
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"ids": [
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"title": "Vulnerability description"
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"text": "Pillow: Pillow: Memory disclosure or denial of service via crafted McIdas AREA image",
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"text": "Pillow is vulnerable to an out-of-bounds read when processing a crafted uncompressed McIdas AREA image via the mmap raw codec. A remote attacker who can supply such an image to a service that loads it without user interaction may disclose adjacent process memory or trigger a denial of service. Fixed in Pillow 12.3.0.",
"title": "Statement"
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"title": "CVSS score applicability"
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
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{
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"title": "Vulnerability summary"
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"title": "Statement"
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{
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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"title": "Statement"
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"category": "general",
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"title": "Pillow: Pillow: Denial of service via crafted PDF stream"
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"id": "CWE-131",
"name": "Incorrect Calculation of Buffer Size"
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"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
]
}
],
"scores": [
{
"cvss_v3": {
"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"
},
"products": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64",
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
]
}
],
"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "Pillow: Pillow: Denial of Service via crafted JPEG2000 image"
},
{
"cve": "CVE-2026-59205",
"cwe": {
"id": "CWE-843",
"name": "Access of Resource Using Incompatible Type (\u0027Type Confusion\u0027)"
},
"discovery_date": "2026-07-14T17:02:11.367224+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500052"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in Pillow, a Python imaging library. This vulnerability allows an attacker to trigger controlled native heap corruption by supplying an output image whose mode does not match the transform\u0027s declared output mode when using the ImageCms.ImageCmsTransform.apply API. This can lead to a denial of service (DoS) condition, making the affected system or application unavailable.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "The impact is limited to denial of service. While the underlying issue is heap corruption via mode mismatch, it does not provide a controlled write primitive exploitable for code execution in affected Red Hat products.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
"fixed": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64",
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
]
},
"references": [
{
"category": "self",
"summary": "Canonical URL",
"url": "https://access.redhat.com/security/cve/CVE-2026-59205"
},
{
"category": "external",
"summary": "RHBZ#2500052",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2500052"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-59205",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-59205"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721",
"url": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/pull/9715",
"url": "https://github.com/python-pillow/Pillow/pull/9715"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0",
"url": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-9hw9-ch79-4vh6",
"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-9hw9-ch79-4vh6"
}
],
"release_date": "2026-07-14T15:48:39.962000+00:00",
"remediations": [
{
"category": "vendor_fix",
"date": "2026-09-23T17:13:39+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:70996",
"product_ids": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64",
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
],
"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:70996"
},
{
"category": "workaround",
"details": "Most applications using Pillow\u0027s color management via profileToProfile() or applyTransform() are not exposed. Only code that calls ImageCmsTransform.apply() directly with a user-controlled output image whose mode does not match the transform can trigger the heap corruption. Audit your code for direct apply() calls to confirm.\n\nRHEL builds ship with ASLR, full RELRO/PIE, and FORTIFY_SOURCE by default, making escalation from crash to code execution much harder. For DoS containment, configure automatic service restart (Restart=always in systemd, or container restart policies) so the process recovers without manual intervention.",
"product_ids": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64",
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
]
}
],
"scores": [
{
"cvss_v3": {
"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"
},
"products": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64",
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
]
}
],
"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API"
},
{
"cve": "CVE-2026-64847",
"cwe": {
"id": "CWE-1322",
"name": "Use of Blocking Code in Single-threaded, Non-blocking Context"
},
"discovery_date": "2026-09-22T17:00:14.782983+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2538074"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in AnyIO. Untrusted or faulty worker code can write a large amount of data to the standard error (stderr) stream. This data fills a pipe that the parent process fails to drain, causing the process-pool call to block indefinitely. This can lead to a Denial of Service (DoS) condition, making the application unresponsive.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "anyio: AnyIO: Denial of Service due to undrained stderr in process-pool workers",
"title": "Vulnerability summary"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
"fixed": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:17ad8c121c110188be68fde0bca2b6fa27f36f3855cb9badd815ef5e5ddf67f9_arm64",
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
]
},
"references": [
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"category": "self",
"summary": "Canonical URL",
"url": "https://access.redhat.com/security/cve/CVE-2026-64847"
},
{
"category": "external",
"summary": "RHBZ#2538074",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2538074"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-64847",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-64847"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-64847",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64847"
},
{
"category": "external",
"summary": "https://github.com/agronholm/anyio/commit/f1b7301c8264b0d2e8d24a5788fd29e93dea4040",
"url": "https://github.com/agronholm/anyio/commit/f1b7301c8264b0d2e8d24a5788fd29e93dea4040"
},
{
"category": "external",
"summary": "https://github.com/agronholm/anyio/pull/1207",
"url": "https://github.com/agronholm/anyio/pull/1207"
},
{
"category": "external",
"summary": "https://github.com/agronholm/anyio/releases/tag/4.14.2",
"url": "https://github.com/agronholm/anyio/releases/tag/4.14.2"
},
{
"category": "external",
"summary": "https://github.com/agronholm/anyio/security/advisories/GHSA-5p39-cfhj-2xmp",
"url": "https://github.com/agronholm/anyio/security/advisories/GHSA-5p39-cfhj-2xmp"
}
],
"release_date": "2026-09-18T17:04:32.038000+00:00",
"remediations": [
{
"category": "vendor_fix",
"date": "2026-09-23T17:13:39+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:70996",
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"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/model-opt-cuda-rhel9@sha256:cea1593ceb68ea72897f544628f83ac92f9c9ab44f16b89aad065971c50fd427_amd64"
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"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:70996"
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"scores": [
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"cvss_v3": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"products": [
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]
}
],
"threats": [
{
"category": "impact",
"details": "Moderate"
}
],
"title": "anyio: AnyIO: Denial of Service due to undrained stderr in process-pool workers"
}
]
}
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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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