RHSA-2026:69464
Vulnerability from csaf_redhat - Published: 2026-09-21 14:29 - Updated: 2026-09-23 18:28Summary
Red Hat Security Advisory: Red Hat AI Inference 3.4.5 (spyre)
Severity
Important
Notes
Topic: Red Hat AI Inference 3.4.5 (spyre) is now available.
Details: Red Hat AI Inference
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
3 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:6b855616de644893c5ee76cd6e496478762f941855f05e47bdac27aa259a7aae_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:8f3b2de1d5a8fdba70e937ff1ab95c0584cec32a4d58b1e636ea130a6461cea1_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b600c4226c78042f1a7d596f8f567dd2d9a97b5b3bb9b008d994c6bfb12bd7f1_s390x | — |
Vendor Fix
fix
Workaround
|
Threats
Impact
Important
A flaw was found in vLLM. An attacker can exploit this vulnerability by sending a specially crafted API request containing an excessive number of base64-encoded JPEG frames within a data URL. This unbounded processing of frames in the `VideoMediaIO.load_base64()` method leads to an Out-of-Memory (OOM) condition, causing the server to crash and resulting in a Denial of Service (DoS).
7.5 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in AIOHTTP, an asynchronous HTTP client/server framework for asyncio and Python. An attacker could exploit this vulnerability by providing untrusted input to the `CookieJar.load()` function. This could potentially lead to arbitrary code execution, allowing the attacker to run malicious code on the affected system. This issue is most likely to occur in applications that allow attacker-controlled files to be loaded.
7.2 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.
7.5 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Pillow, a Python imaging library. An attacker could exploit this vulnerability by tricking a user into processing a specially crafted malicious PSD file. This could lead to memory corruption, potentially causing the application to crash or allowing for arbitrary code execution.
7.8 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The extract_hidden_states speculative decoding proposer returns a tensor with an incorrect shape after the first decode step. This can be triggered by a remote attacker sending a request that uses sampling penalty parameters, such as repetition_penalty, frequency_penalty, or presence_penalty. Successful exploitation leads to a RuntimeError that crashes the EngineCore process, resulting in a denial of service (DoS).
7.5 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in PyJWT, a Python library for JSON Web Token (JWT) implementation. When decoding JWTs, the library fails to validate the use of JSON Web Keys (JWK) in the HMAC algorithm while also supporting asymmetric algorithms. This allows a remote attacker to use the issuer's public key as the secret key for the HMAC algorithm, leading to the ability to forge JWTs. This vulnerability can result in authentication bypass or unauthorized access.
7.4 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Starlette, a lightweight ASGI (Asynchronous Server Gateway Interface) framework. A remote attacker could exploit this vulnerability by sending a specially crafted HTTP `Host` request header. This malformed header could cause the `request.url` to be incorrectly reconstructed, leading to a discrepancy with the actual requested path. Consequently, security restrictions enforced by middleware and endpoints that rely on `request.url` for validation could be bypassed, potentially allowing unauthorized access or actions.
6.5 (Medium)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Exploit Status
CISA: https://www.cisa.gov/known-exploited-vulnerabilities-catalog
Impact
Moderate
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). This vulnerability, residing in ASGI web servers and Starlette's trust in them, allows an attacker to bypass the OpenAI API Authentication Middleware. This bypass enables unauthorized access to the API without requiring the configured VLLM_API_KEY or --api-key, leading to critical unauthorized operations.
9.1 (Critical)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
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
3 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
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). A remote attacker can exploit this vulnerability by sending a specially crafted multi-request speculative decoding workload through public gRPC Generate and Abort endpoints. This malformed workload can cause the rejection sampler to produce an out-of-vocabulary token, which then crashes the engine worker. This leads to a service-wide Denial of Service (DoS) for all clients until the worker is restarted.
7.5 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in Starlette where the request.form() method silently ignores configured resource limits (max_fields and max_part_size) when parsing application/x-www-form-urlencoded data. An unauthenticated attacker can exploit this by sending a urlencoded request body with an arbitrarily large number of fields or an oversized field, causing denial of service through resource exhaustion.
7.5 (High)
Affected products
Fixed
3 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
3 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
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.
7.5 (High)
Affected products
Fixed
3 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
3 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
3 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
3 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
3 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
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
A flaw was found in vLLM, an inference and serving engine for large language models. An unauthenticated remote attacker can exploit a Regular Expression Denial of Service (ReDoS) vulnerability in the lm-format-enforcer backend. By submitting a specially crafted regular expression through the structured_outputs.regex parameter, the attacker can cause the system to consume excessive CPU resources. This leads to a denial of service, stalling the structured-output engine and preventing legitimate requests from being processed.
7.5 (High)
Affected products
Fixed
3 products, the same list as for
CVE-2026-5241
Threats
Impact
Important
References
177 references
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"text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
"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": {
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"summary": "Canonical URL",
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"category": "external",
"summary": "RHBZ#2491582",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491582"
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"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-41523",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-41523"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3",
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"category": "external",
"summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r"
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"summary": "https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c",
"url": "https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c"
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"date": "2026-09-21T14:29:35+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:69464",
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"category": "workaround",
"details": "Avoid running vLLM with python -O or PYTHONOPTIMIZE=1 until updated packages are available. Only load models from trusted sources. Restrict who can deploy or update models on inference endpoints. Apply network access controls and authentication in front of vLLM APIs.",
"product_ids": [
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"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
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"details": "Important"
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"title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
},
{
"cve": "CVE-2026-42311",
"cwe": {
"id": "CWE-120",
"name": "Buffer Copy without Checking Size of Input (\u0027Classic Buffer Overflow\u0027)"
},
"discovery_date": "2026-05-09T06:01:14.891885+00:00",
"ids": [
{
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"text": "2468459"
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{
"category": "description",
"text": "A flaw was found in Pillow, a Python imaging library. An attacker could exploit this vulnerability by tricking a user into processing a specially crafted malicious PSD file. This could lead to memory corruption, potentially causing the application to crash or allowing for arbitrary code execution.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: python-pillow: Pillow: Arbitrary code execution via malicious PSD file processing",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This is an Important severity flaw in Pillow, a Python imaging library, that could lead to arbitrary code execution. The vulnerability requires a user to process a specially crafted malicious PSD file, which could result in memory corruption and allow an attacker to execute arbitrary code. Red Hat products that process untrusted image files using Pillow are at risk if they do not adequately sanitize input or operate in a sandboxed environment.",
"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"
}
],
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"summary": "Canonical URL",
"url": "https://access.redhat.com/security/cve/CVE-2026-42311"
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"summary": "RHBZ#2468459",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2468459"
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"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-42311",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-42311"
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{
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"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-42311",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-42311"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/58f9a1d166dcb0c274807d4423522d205b0c35ea",
"url": "https://github.com/python-pillow/Pillow/commit/58f9a1d166dcb0c274807d4423522d205b0c35ea"
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"category": "external",
"summary": "https://github.com/python-pillow/Pillow/pull/9520",
"url": "https://github.com/python-pillow/Pillow/pull/9520"
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"summary": "https://github.com/python-pillow/Pillow/releases/tag/12.2.0",
"url": "https://github.com/python-pillow/Pillow/releases/tag/12.2.0"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-pwv6-vv43-88gr",
"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-pwv6-vv43-88gr"
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"date": "2026-09-21T14:29:35+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:69464",
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"details": "To mitigate this vulnerability, users should avoid processing untrusted or suspicious PSD image files with applications that utilize the Pillow library. Implementing strict input validation and sanitization for image uploads and processing workflows can reduce the risk. Additionally, running applications that process untrusted content within a sandboxed environment can limit the potential impact of successful exploitation.",
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"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
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{
"cve": "CVE-2026-44223",
"cwe": {
"id": "CWE-130",
"name": "Improper Handling of Length Parameter Inconsistency"
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"discovery_date": "2026-05-12T21:02:06.713372+00:00",
"ids": [
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"text": "2476827"
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"title": "Vulnerability description"
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{
"category": "summary",
"text": "vllm: vLLM: Denial of Service via malformed tensor shape in speculative decoding",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important denial of service flaw in vLLM, as used in Red Hat AI Inference Server and Red Hat OpenShift AI, allows a remote attacker to crash the EngineCore process. By sending a request with specific sampling penalty parameters, an attacker can trigger an incorrect tensor shape, leading to a service disruption for affected AI inference workloads.",
"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"
}
],
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{
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"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-44223",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44223"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/pull/38610",
"url": "https://github.com/vllm-project/vllm/pull/38610"
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"date": "2026-09-21T14:29:35+00:00",
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"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",
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"cve": "CVE-2026-48526",
"cwe": {
"id": "CWE-347",
"name": "Improper Verification of Cryptographic Signature"
},
"discovery_date": "2026-05-28T16:01:22.805235+00:00",
"ids": [
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"text": "2482734"
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"category": "description",
"text": "A flaw was found in PyJWT, a Python library for JSON Web Token (JWT) implementation. When decoding JWTs, the library fails to validate the use of JSON Web Keys (JWK) in the HMAC algorithm while also supporting asymmetric algorithms. This allows a remote attacker to use the issuer\u0027s public key as the secret key for the HMAC algorithm, leading to the ability to forge JWTs. This vulnerability can result in authentication bypass or unauthorized access.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "python-pyjwt: PyJWT: Authentication bypass due to forged JSON Web Tokens",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important vulnerability in PyJWT allows for authentication bypass and unauthorized access. It occurs when a JWT verifier is misconfigured to accept both symmetric and asymmetric algorithms, and a public JSON Web Key is erroneously used as the HMAC secret. Red Hat products are only affected if they utilize this specific, non-standard verifier configuration.",
"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"
}
],
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"category": "summary",
"text": "starlette: Starlette: Security restriction bypass via malformed HTTP Host header",
"title": "Vulnerability summary"
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"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"
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"name": "Trust Boundary Violation"
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"title": "Vulnerability description"
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"title": "Statement"
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{
"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"
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"name": "Out-of-bounds Read"
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"ids": [
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"text": "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.",
"title": "Vulnerability description"
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"text": "Pillow: Pillow: Memory disclosure or denial of service via crafted McIdas AREA image",
"title": "Vulnerability summary"
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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"
},
{
"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"
}
],
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]
}
],
"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "Pillow: Pillow: Denial of service via crafted PDF stream"
},
{
"cve": "CVE-2026-59204",
"cwe": {
"id": "CWE-131",
"name": "Incorrect Calculation of Buffer Size"
},
"discovery_date": "2026-07-14T16:02:11.802781+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2500022"
}
],
"notes": [
{
"category": "description",
"text": "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.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "Pillow: Pillow: Denial of Service via crafted JPEG2000 image",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "The impact is limited to denial of service via excessive memory allocation. A crafted JPEG2000 file can trigger out-of-memory failures, but this does not lead to code execution or information disclosure.",
"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/vllm-spyre-rhel9@sha256:6b855616de644893c5ee76cd6e496478762f941855f05e47bdac27aa259a7aae_ppc64le",
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]
},
"references": [
{
"category": "self",
"summary": "Canonical URL",
"url": "https://access.redhat.com/security/cve/CVE-2026-59204"
},
{
"category": "external",
"summary": "RHBZ#2500022",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2500022"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-59204",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-59204"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-59204",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59204"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/13ada41172142f2fd9f0906f615a00ea623a11ca",
"url": "https://github.com/python-pillow/Pillow/commit/13ada41172142f2fd9f0906f615a00ea623a11ca"
},
{
"category": "external",
"summary": "https://github.com/python-pillow/Pillow/pull/9704",
"url": "https://github.com/python-pillow/Pillow/pull/9704"
},
{
"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-vjc4-5qp5-m44j",
"url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-vjc4-5qp5-m44j"
}
],
"release_date": "2026-07-14T15:38:29.545000+00:00",
"remediations": [
{
"category": "vendor_fix",
"date": "2026-09-21T14:29:35+00:00",
"details": "For more information visit https://access.redhat.com/errata/RHSA-2026:69464",
"product_ids": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:6b855616de644893c5ee76cd6e496478762f941855f05e47bdac27aa259a7aae_ppc64le",
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"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:69464"
},
{
"category": "workaround",
"details": "If the application does not need JPEG2000 support, block .jp2, .j2k, .jpf, and .jpx uploads at the input layer.\n\nFor services that do process JPEG2000, set memory limits on the process or container (LimitAS= in systemd, or memory limits in Kubernetes/Podman) so a crafted image can only crash the worker, not the whole host. Add automatic restarts (Restart=always in systemd, or container restart policies) so the service recovers from OOM kills without someone having to intervene.",
"product_ids": [
"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:6b855616de644893c5ee76cd6e496478762f941855f05e47bdac27aa259a7aae_ppc64le",
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"scores": [
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"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": [
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"threats": [
{
"category": "impact",
"details": "Important"
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"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"
}
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"notes": [
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"category": "description",
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"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": [
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"references": [
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"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2500052"
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"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-59205",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-59205"
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{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205"
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"category": "external",
"summary": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721",
"url": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721"
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"category": "external",
"summary": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0",
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"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": [
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"scores": [
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"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"
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"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API"
},
{
"cve": "CVE-2026-73556",
"cwe": {
"id": "CWE-1333",
"name": "Inefficient Regular Expression Complexity"
},
"discovery_date": "2026-08-13T15:13:23.640309+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2515404"
}
],
"notes": [
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"category": "description",
"text": "A flaw was found in vLLM, an inference and serving engine for large language models. An unauthenticated remote attacker can exploit a Regular Expression Denial of Service (ReDoS) vulnerability in the lm-format-enforcer backend. By submitting a specially crafted regular expression through the structured_outputs.regex parameter, the attacker can cause the system to consume excessive CPU resources. This leads to a denial of service, stalling the structured-output engine and preventing legitimate requests from being processed.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Denial of Service via Regular Expression processing",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This flaw in vLLM is rated as Important because an unauthenticated remote attacker can trigger a Regular Expression Denial of Service (ReDoS). By sending a specially crafted regular expression to the `/v1/completions` endpoint, the attacker can cause the vLLM service to consume excessive CPU resources, leading to a complete stall of the structured-output engine and preventing legitimate requests.",
"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"
}
],
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-73556"
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"url": "https://github.com/vllm-project/vllm/pull/47595"
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"scores": [
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"baseScore": 7.5,
"baseSeverity": "HIGH",
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "vllm: vLLM: Denial of Service via Regular Expression processing"
}
]
}
Loading…
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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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