Vulnerabilities

With the aim of informing, warning and helping professionals with the latest security vulnerabilities in technology systems, we have made a database available for users interested in this information, which is in Spanish and includes all of the latest documented and recognised vulnerabilities.

This repository, with over 75,000 registers, is based on the information from the NVD (National Vulnerability Database) – by virtue of a partnership agreement – through which INCIBE translates the included information into Spanish.

On occasions this list will show vulnerabilities that have still not been translated, as they are added while the INCIBE team is still carrying out the translation process. The CVE (Common Vulnerabilities and Exposures) Standard for Information Security Vulnerability Names is used with the aim to support the exchange of information between different tools and databases.

All vulnerabilities collected are linked to different information sources, as well as available patches or solutions provided by manufacturers and developers. It is possible to carry out advanced searches, as there is the option to select different criteria to narrow down the results, some examples being vulnerability types, manufacturers and impact levels, among others.

Through RSS feeds or Newsletters we can be informed daily about the latest vulnerabilities added to the repository. Below there is a list, updated daily, where you can discover the latest vulnerabilities.

CVE-2026-55391

Publication date:

28/07/2026

datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. Prior to 0.63.0, datamodel-code-generator validates a URL host once in src/datamodel_code_generator/http.py through get_body, _validate_url_for_fetch, and _get_ips_from_host, but then lets httpx resolve the host again for the connection, allowing DNS rebinding to bypass allow_private_network=False and reach internal services. This issue is fixed in version 0.63.0.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-55403

Publication date:

28/07/2026

datamodel-code-generator generates Python data models from schema definitions. Prior to 0.63.0, src/datamodel_code_generator/http.py get_body reuses Authorization, Cookie, and Proxy-Authorization headers when following cross-origin redirects while fetching remote schemas, allowing credentials scoped to one schema host to be leaked to another redirect target. This issue is fixed in version 0.63.0.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-55415

Publication date:

28/07/2026

datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.11.6 until 0.64.0, datamodel-code-generator allows attacker-controlled x-python-import or customTypePath schema extensions to reach src/datamodel_code_generator/parser/jsonschema.py and generated import handling through Import.from_full_path and Imports.create_line in src/datamodel_code_generator/imports.py, allowing a newline to break out of an import statement and execute Python code when the generated model is imported. This issue is fixed in version 0.64.0.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54653

Publication date:

28/07/2026

datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.17.0 until 0.60.2, datamodel-code-generator preserves attacker-controlled default_factory values in src/datamodel_code_generator/parser/jsonschema.py through JsonSchemaObject.init and get_field_extras and emits them into Field(default_factory=...) or field(default_factory=...), allowing Python expression execution when the generated model is imported. This issue is fixed in version 0.60.2.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54654

Publication date:

28/07/2026

datamodel-code-generator generates Python data models from schema definitions. From 0.14.1 until 0.60.2, the --extra-template-data comment field is rendered into Python comments in src/datamodel_code_generator/model/template/TypeAliasAnnotation.jinja2, src/datamodel_code_generator/model/template/TypedDict.jinja2, src/datamodel_code_generator/model/template/dataclass.jinja2, src/datamodel_code_generator/model/template/msgspec.Struct.jinja2, src/datamodel_code_generator/model/template/pydantic/BaseModel.jinja2, and src/datamodel_code_generator/model/template/pydantic_v2/BaseModel.jinja2 without neutralizing carriage returns in Python # comments, allowing an attacker-controlled comment value to inject Python code into generated models that runs when imported. This issue is fixed in version 0.60.2.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54655

Publication date:

28/07/2026

datamodel-code-generator generates Python data models from schema definitions. From 0.51.0 until 0.60.2, x-python-type values parsed by src/datamodel_code_generator/parser/jsonschema.py in _get_python_type_override are inserted into generated field annotations without sufficient validation, allowing attacker-controlled JSON Schema content to execute Python code when the generated module is imported. This issue is fixed in version 0.60.2.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54656

Publication date:

28/07/2026

datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.52.1 until 0.60.2, datamodel-code-generator interpolates validators from --extra-template-data in src/datamodel_code_generator/model/pydantic_v2/base_model.py through _process_validators into @field_validator decorators without safe validation, allowing Python code execution when the generated Pydantic v2 model is imported. This issue is fixed in version 0.60.2.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54690

Publication date:

28/07/2026

datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. From 0.9.1 until 0.61.0, datamodel-code-generator silently dereferences attacker-controlled JSON Schema $ref HTTP or HTTPS URLs in src/datamodel_code_generator/parser/jsonschema.py through _get_ref_body, and the --allow-remote-refs gate can warn instead of blocking, allowing server-side request forgery through src/datamodel_code_generator/http.py. This issue is fixed in version 0.61.0.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54691

Publication date:

28/07/2026

datamodel-code-generator generates Python data models from schema definitions. From 0.9.1 until 0.61.0, src/datamodel_code_generator/http.py http.get_body accepts --url targets and redirect chain targets without host/IP validation, allowing server-side request forgery against loopback, private, link-local, metadata, and other network-accessible resources. This issue is fixed in version 0.61.0.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-54621

Publication date:

28/07/2026

datamodel-code-generator generates Python data models from schema definitions. Prior to 0.60.1, GraphQL Union description values in src/datamodel_code_generator/model/template/UnionTypeStatement.jinja2 and src/datamodel_code_generator/model/template/UnionTypeStatement.py312.jinja2 are rendered into Python comments without neutralizing carriage returns in Python # comments, allowing attacker-controlled GraphQL schema content to inject Python code into generated models that runs when imported. This issue is fixed in version 0.60.1.

Severity CVSS v4.0: Pending analysis

Last modification:

28/07/2026

CVE-2026-59941

Publication date:

28/07/2026

Dompdf is an HTML to PDF converter for PHP. Versions 3.15 and prior accept a BMP image and generates a PDF-compatible PNG based only on its declared header dimensions and never bounds width × height before the image is converted through GD. A 58-byte BMP whose header declares e.g. 6000×6000 is accepted and later drives imagecreatetruecolor($width, $height) (and PHP&#39;s native BMP decoder) to allocate the full pixel canvas. A payload can fit in a single HTTP request: the BMP can be inlined as a data:image/bmp;base64,… URI inside attacker-controlled HTML, so no upload, no remote fetch, and no chroot-reachable file is required. I measured a 169-byte request driving a dompdf render to ~412 MB peak RSS and ~4.8 s of CPU/wall time, versus ~34 MB for an identically-sized benign request — roughly a 12× memory amplification per request, repeatable and unauthenticated. This issue has been fixed in version 3.16.

Severity CVSS v4.0: MEDIUM

Last modification:

28/07/2026

CVE-2026-59942

Publication date:

28/07/2026

Dompdf is an HTML to PDF converter for PHP. Versions 3.15 and prior are vulnerable to a Denial of Service (DoS) attack via resource exhaustion. An attacker can crash the PHP process by providing a specially crafted HTML document containing a single image with massive dimensions (e.g., 30,000x30,000 pixels). While Dompdf implements internal checks to validate image dimensions, these can be bypassed by using a high-entropy image (such as random noise) encoded in Base64 and wrapped in specific CSS containers. The vulnerability exists because the dimension validation happens early, but the resource allocation for calculating the object&#39;s bounding box and internal buffers during the rendering phase does not strictly limit the cumulative CPU time or memory usage for a single object that has passed the initial check. An unauthenticated remote attacker can cause a complete Denial of Service on the web server by submitting a crafted HTML string. This affects any application that allows users to provide HTML content or URLs that are subsequently converted to PDF using Dompdf. This issue has been fixed in version 3.16.

Severity CVSS v4.0: MEDIUM

Last modification:

28/07/2026

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