Veille CVE

Lundi 4 mai 2026

10 CVE

  1. CVE-2026-42221 9.8 Critique

    0xJacky nginx-ui · Nginxui nginx ui

    Nginx UI is a web user interface for the Nginx web server. From version 2.0.0 to before version 2.3.8, an unauthenticated network attacker can claim the initial administrator account on a fresh nginx-ui instance during the first-run setup window. The public /api/install endpoint is reachable without authentication, and the request-encryption flow only protects payload confidentiality in transit; it does not authenticate who is allowed to perform installation. A remote attacker who reaches the service before the legitimate operator can set the admin email, username, and password, causing permanent initial-instance takeover. This issue has been patched in version 2.3.8.

  2. CVE-2026-42231 8.8 Élevée

    n8n-io n8n · N8n n8n

    n8n is an open source workflow automation platform. Prior to versions 1.123.32, 2.17.4, and 2.18.1, a flaw in the xml2js library used to parse XML request bodies in n8n's webhook handler allowed prototype pollution via a crafted XML payload. An authenticated user with permission to create or modify workflows could exploit this to pollute the JavaScript object prototype and, by chaining the pollution with the Git node's SSH operations, achieve remote code execution on the n8n host. This issue has been patched in versions 1.123.32, 2.17.4, and 2.18.1.

  3. CVE-2026-42228 6.5 Moyenne

    n8n-io n8n · N8n n8n

    n8n is an open source workflow automation platform. Prior to versions 1.123.32, 2.17.4, and 2.18.1, the /chat WebSocket endpoint used by the Chat Trigger node's Hosted Chat feature did not verify that an incoming connection was authorized to interact with the target execution. An unauthenticated remote attacker who could identify a valid execution ID for a workflow in a waiting state could attach to that execution, receive the pending prompt intended for the legitimate user, and submit arbitrary input to resume or influence downstream workflow behavior. This issue has been patched in versions 1.123.32, 2.17.4, and 2.18.1.

  4. CVE-2026-42084 8.1 Élevée

    OpenC3 cosmos

    OpenC3 COSMOS provides the functionality needed to send commands to and receive data from one or more embedded systems. Prior to versions 6.10.5 and 7.0.0-rc3, the OpenC3 password change functionality allows a user to change their password without providing the old password, by accepting a valid session token instead. In assumed breach scenarios, this behaviour can be exploited by an attacker who has already obtained a valid session token, to gain persistence in hijacked account (including admin) and prevent legitimate users from accessing the account. This issue has been patched in versions 6.10.5 and 7.0.0-rc3.

  5. CVE-2026-42440 7.5 Élevée

    Apache Software Foundation Apache OpenNLP · Red Hat Data Grid 8 · Red Hat Fuse 7 · Red Hat JBoss Enterprise Application Platform Expansion Pack

    OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader  Versions Affected:  before 1.9.5 before 2.5.9 before 3.0.0-M3  Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound. The count is therefore fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream. The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.   Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Users who cannot upgrade immediately should treat all .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.

  6. CVE-2026-42027 9.8 Critique

    Apache Software Foundation Apache OpenNLP · Red Hat OpenShift AI 2.25 · Red Hat build of Apache Camel for Spring Boot 4 · Red Hat Data Grid 8

    Arbitrary Class Instantiation via Model Manifest in Apache OpenNLP ExtensionLoader Versions Affected: before 1.9.5, before 2.5.9, before 3.0.0-M3 Description:  The ExtensionLoader.instantiateExtension(Class, String) method loads a class by its fully-qualified name via Class.forName() and invokes its no-arg constructor, with the class name sourced from the manifest.properties entry of a model archive. The existing isAssignableFrom check correctly rejects classes that are not subtypes of the expected extension interface (BaseToolFactory for factory=, ArtifactSerializer for serializer-class-*), but the check runs after Class.forName() has already loaded and initialized the named class. Class.forName() with default initialization semantics executes the target class's static initializer before returning, so an attacker who can supply a crafted model archive can cause the static initializer of any class on the classpath to run during model loading, regardless of whether that class passes the subsequent type check. Exploitation requires a class with attacker-useful side effects in its static initializer (for example, JNDI lookup, outbound network I/O, or filesystem access) to be present on the classpath, so this is not a drop-in remote code execution; however, the attack surface grows as third-party model distribution becomes more common (community model repositories, Hugging Face-style sharing), where users routinely load model files from origins they do not control. A secondary, narrower vector affects deployments that ship legitimate BaseToolFactory or ArtifactSerializer subclasses with side-effecting no-arg constructors: a malicious manifest can name such a class and force its constructor to run during model load. Mitigation:  * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces a package-prefix allowlist that is consulted before Class.forName() is invoked, so the static initializer of a disallowed class is never executed. Classes under the opennlp. prefix remain permitted by default. Deployments that load models referencing factories or serializers outside opennlp.* must opt those packages in, either programmatically via ExtensionLoader.registerAllowedPackage(String) before the first model load, or by setting the OPENNLP_EXT_ALLOWED_PACKAGES system property to a comma-separated list of allowed package prefixes. Users who cannot upgrade immediately should ensure that all model files are sourced from trusted origins and should audit their classpath for classes with side-effecting static initializers or constructors, particularly any that perform JNDI lookups, network requests, or filesystem operations during class initialization.

  7. CVE-2025-47407 7.0 Élevée

    Qualcomm, Inc. Snapdragon · Qualcomm video collaboration vc1 platform firmware · Qualcomm video collaboration vc3 platform firmware · Qualcomm qxm1083 firmware

    Memory corruption while creating a process on the digital signal processor due to allocation failure at the kernel level.

  8. CVE-2025-47401 7.5 Élevée

    Qualcomm, Inc. Snapdragon · Qualcomm fastconnect 6200 firmware · Qualcomm fastconnect 6700 firmware · Qualcomm fastconnect 6900 firmware

    Transient DOS when processing target power rate tables during channel configuration.

  9. CVE-2025-58074 8.8 Élevée

    Gen Digital Norton Secure VPN

    A privilege escalation vulnerability exists during the installation of Norton Secure VPN via the Microsoft Store. A low-privilege user can replace files during the installation process, which may result in deletion of arbitrary files that can lead to elevation of privileges.

  10. CVE-2026-7725 6.3 Moyenne

    PrefectHQ prefect

    A vulnerability was found in PrefectHQ prefect up to 3.6.25.dev6. Affected by this issue is some unknown functionality of the file src/prefect/runner/storage.py of the component GitRepository Pull Handler. The manipulation of the argument commit_sha/directories results in argument injection. It is possible to launch the attack remotely. The exploit has been made public and could be used. Upgrading to version 3.6.25.dev7 can resolve this issue. The patch is identified as 6a9d9918716ce4ee0297b69f3046f7067ef1faae. It is advisable to upgrade the affected component. The vendor was contacted early, responded in a very professional manner and quickly released a fixed version of the affected product.