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| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2026-54653 | 8.8 high | 1.0% | koxudaxi, datamodel-code-generator | 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. | Fri 23 Oct |
| CVE-2026-55389 | 7.5 high | 1.0% | koxudaxi, datamodel-code-generator | 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 0.62.0, datamodel-code-generator resolves JSON Schema $ref targets in src/datamodel_code_generator/parser/jsonschema.py through is_url and _get_ref_body without containing file:// or ../ traversal references to the input directory and without honoring --no-allow-remote-refs, allowing arbitrary local file reads. This issue is fixed in version 0.62.0. | Fri 23 Oct |
| CVE-2026-107290 | 6.5 medium | 1.0% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.77.0 until 1.107.6 and 2.44.0, the local web_fetch_tool and the WebFetch local fallback process server-controlled responses with quadratic title extraction, whitespace normalization, and ordered-list numbering. An attacker-controlled page of modest size can therefore block the event loop for an extended period, stalling other agent runs and requests, while unsupported codecs or excessive HTML or JSON nesting can abort an individual run. This issue is fixed in versions 1.107.6 and 2.44.0. | 13 Oct |
| CVE-2026-107291 | 2.3 low | 1.0% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 0.3.4 until 1.107.6 and 2.44.0, OpenTelemetry instrumentation configured with InstrumentationSettings(include_content=False) can still export sensitive agent content through exception.message and exception.stacktrace events, error status descriptions, and model_request_parameters containing instructions or the prompted_output_template. The exposed data is available to readers of the configured telemetry backend and can include tool feedback, provider error bodies, runtime instructions, and structured-output templates even though message attributes are redacted. This issue does not grant new access to agent data, and deployments that do not use include_content=False are not affected by the setting bypass. This issue is fixed in versions 1.107.6 and 2.44.0. | 13 Oct |
| CVE-2026-108381 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.44.0 through 0.70.5, the Pydantic and AvroDantic model generators insert a schema field's pydantic-class value after pydantic. in a generated field annotation without safely restricting the expression. Because annotations are evaluated when the generated module is imported, a crafted value can execute arbitrary Python code. Version 0.70.6 fixes the vulnerability by restricting the value to a bare name or a call to a bare name with literal arguments. | 16 Oct |
| CVE-2026-108376 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.47.3 through 0.70.5, FieldRepresentation.get_field_default inserts a schema-controlled referenced record name into the callable position of a generated default_factory lambda without validating it as an Avro name. A crafted record name can become an arbitrary Python expression that executes when an instance of the generated model consumes the field default. Version 0.70.6 fixes the vulnerability by rejecting record names that do not match the Avro name grammar. | 16 Oct |
| CVE-2026-108379 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, BaseGenerator.parse_fixed inserts a fixed type's namespace value between quotes inside a generated types.confixed annotation without escaping or validating it. A crafted quote can terminate the namespace string and alter the surrounding annotation expression, which Python evaluates when the generated class is defined, allowing arbitrary code execution. Version 0.70.6 fixes the vulnerability by requiring the namespace to be a dot-separated sequence of valid Avro names. | 16 Oct |
| CVE-2026-108380 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.65.9 through 0.70.5, PydanticFieldRepresentation.add_field_properties inserts a schema field's doc value between the quotes of a generated pydantic.Field description argument without escaping it. A crafted quote can terminate the string and inject an expression evaluated during generated class definition, allowing arbitrary Python code execution for Pydantic and AvroDantic model generation. Version 0.70.6 fixes the vulnerability by escaping the documentation value before rendering it. | 16 Oct |
| CVE-2026-108384 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, BaseGenerator.parse_fixed interpolates a fixed type's aliases value directly into a generated types.confixed annotation. A list is represented with quoted entries, but a bare string is emitted as an unquoted Python expression and evaluated when the generated module is imported, allowing arbitrary code execution from a crafted schema. Version 0.70.6 fixes the vulnerability by rejecting aliases values that are not lists. | 16 Oct |
| CVE-2026-108385 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, BaseGenerator.parse_enum and EnumRepresentation.render_symbols use each schema-controlled enum symbol as a generated enum member name after case conversion but without validating it as an identifier. When generator classes are used directly, newlines and punctuation can inject constrained statements into the generated enum class body, allowing namespace modification or preventing the module from loading. Version 0.70.6 fixes the vulnerability by validating every enum symbol as a name. | 16 Oct |
| CVE-2026-108375 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, FieldRepresentation.get_field_default inserts schema-controlled string, UUID, bytes, and fixed field defaults into quoted literals in generated Python source without escaping them. A crafted default can terminate the literal and inject statements into the generated module, which execute when the module is imported. Version 0.70.6 fixes the vulnerability by escaping schema values rendered into generated string literals. | 16 Oct |
| CVE-2026-108377 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.62.3 through 0.70.5, ClassRepresentation.render_metaclass inserts a schema-controlled Avro record name into the quoted Meta.schema_name field of generated Python source without escaping it. A crafted name can terminate the string literal and inject statements into the generated class body, which execute when the generated module is imported. Version 0.70.6 fixes the vulnerability by escaping schema values rendered into generated string literals. | 16 Oct |
| CVE-2026-108378 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.40.0 through 0.70.5, ClassRepresentation.render_docstring copies a schema record's doc value into a generated triple-double-quoted class docstring without escaping quotes or backslashes. A crafted triple-quote sequence can close the docstring and inject statements into the generated class body, while a trailing backslash can escape the generated closing delimiter, allowing Python code execution during module import. Version 0.70.6 fixes the vulnerability by escaping quotes, backslashes, and unsafe control characters before rendering docstrings. | 16 Oct |
| CVE-2026-108382 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, EnumRepresentation.render_symbols concatenates each schema-controlled enum symbol between double quotes in a generated enum assignment. When generator classes are used directly, a quote can terminate the string literal and inject arbitrary Python code into the enum class body, which executes when the generated module is imported. Version 0.70.6 fixes the vulnerability by escaping enum symbol values before rendering them. | 16 Oct |
| CVE-2026-108383 | 8.8 high | 0.00% | no CPE yet | dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, FieldRepresentation.get_field_default renders a schema-controlled enum field default as an enum member reference after applying case conversion but without validating it as an identifier. Newlines, operators, and punctuation can escape the intended reference and inject constrained statements into the generated module, allowing namespace modification or preventing the module from loading. Version 0.70.6 fixes the vulnerability by validating enum defaults as names. | 16 Oct |
| CVE-2026-61437 | 8.5 high | 0.00% | no CPE yet | PraisonAI (pip package praisonaiagents) before 1.6.78 contains an unsafe dynamic module loading vulnerability in AgentFlow._resolve_pydantic_class (src/praisonai-agents/praisonaiagents/workflows/workflows.py). When a workflow step uses a string output_pydantic reference, the framework locates and imports a sibling tools.py from the workflow file's directory via importlib exec_module without sandboxing, ignoring the PRAISONAI_ALLOW_*_TOOLS environment variables. An attacker who controls a workflow file and its sibling tools.py can execute arbitrary Python code with the workflow runner's privileges when the workflow is executed via WorkflowManager or after load_yaml. | Sun 25 Oct |
| CVE-2026-54690 | 8.2 high | 0.00% | koxudaxi, datamodel-code-generator | 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. | Fri 23 Oct |
| CVE-2026-54654 | 7.8 high | 0.00% | no CPE yet | 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. | Fri 23 Oct |
| CVE-2026-54656 | 7.8 high | 0.00% | koxudaxi, datamodel-code-generator | 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. | Fri 23 Oct |
| CVE-2026-107295 | 7.6 high | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.34.0 until 1.107.4 and 2.28.0, the Agent.to_web() and clai web development chat endpoint has missing request content-type validation. A website visited by a developer can submit a browser-compatible request to a loopback-hosted chat server, causing the served agent to run and execute tools with the privileges and credentials of the local process; client-relayed approval decisions also leave requires_approval=True tools exposed. Binding to localhost does not prevent a browser page from reaching the loopback address. This issue is fixed in versions 1.107.4 and 2.28.0. | 13 Oct |
| CVE-2026-68968 | 7.5 high | 0.00% | apache, airflow | Apache Airflow's Backfill API authorized a request against a Dag id supplied by the caller whenever the `backfill_id` path segment failed to parse. The authorization dependency parsed it with `int()` while the route handler parsed it as pydantic's `NonNegativeInt`, which accepts values `int()` rejects (`1.0` coerces to `1`); FastAPI resolves dependencies before endpoint validation, so the two acted on different Dags. An authenticated user holding edit permission on any single Dag could therefore read, pause and cancel backfills belonging to any other Dag, including moving another Dag's queued runs to `failed`. No non-default configuration is required and backfill ids are sequential, so finding a target is trivial. Users are advised to upgrade to apache-airflow 3.3.1 or later, which parses the backfill id with the same type the routes declare. | Sat 24 Oct |
| CVE-2026-55391 | 7.5 high | 0.00% | koxudaxi, datamodel-code-generator | 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. | Fri 23 Oct |
| CVE-2026-55415 | 7.5 high | 0.00% | koxudaxi, datamodel-code-generator | 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. | Fri 23 Oct |
| CVE-2026-107286 | 7.5 high | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 2.10.0 until 2.53.0, streamed requests made through ConcurrencyLimitedModel or limit_model_concurrency can retain shared concurrency slots because anyio.CapacityLimiter associates an acquired slot with the borrowing task while streaming cleanup can run in a different task. Early stream termination, cancellation, consumer exceptions, or complete stream_text() consumption with debounce_by=0.1 can therefore leave capacity occupied, eventually preventing later requests that share the long-lived limiter from proceeding and causing a denial of service. Agent-level max_concurrency and non-streaming model requests are not affected. This issue is fixed in version 2.53.0. | 13 Oct |
| CVE-2026-57114 | 7.2 high | 0.00% | no CPE yet | PraisonAI is a multi-agent teams system. From version 4.5.126 to before version 4.6.59, PraisonAI's Async Jobs API validates webhook_url when a job request is parsed and again when the internal Job object is constructed. That validation blocks direct loopback/private targets, but it is not bound to the later network request. When a job completes, _send_webhook() passes the original hostname to httpx.AsyncClient.post() with no send-time validation, IP pinning, or guarded transport. An attacker-controlled hostname can therefore resolve to a public IP during Pydantic validation and later resolve to loopback/private/cloud-metadata infrastructure during webhook delivery. This bypasses the intended SSRF guard in current supported releases. This appears to be an incomplete fix / patch bypass for GHSA-8frj-8q3m-xhgm. This issue has been patched in version 4.6.59. | Fri 23 Oct |
| CVE-2026-107289 | 6.8 medium | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.56.0 until 1.107.6 and 2.44.0, applications that opt attacker-influenced URLs into local network access through FileUrl with force_download='allow-local' or web_fetch_tool with allow_local_urls=True can bypass the cloud-metadata blocklist by appending an IPv6 zone identifier to an IPv6 metadata address. IPv6Address equality and hashing include the zone identifier, so the blocklist comparison fails even though the network stack ignores the zone on a non-link-local destination and reaches the metadata service, potentially exposing cloud IAM credentials. The opt-in settings are disabled by default, and the issue requires an IPv6-enabled environment. This issue is fixed in versions 1.107.6 and 2.44.0. | 13 Oct |
| CVE-2026-107287 | 6.5 medium | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.77.0 until 1.107.7 and 2.52.0, the local web_fetch_tool and the WebFetch local fallback can consume excessive CPU and memory during HTML-to-Markdown conversion of attacker-controlled HTML containing deeply nested block elements. Conversion repeatedly reprocesses accumulated text and can greatly expand intermediate output before the returned-content limit is applied, allowing a model-directed fetch to delay other work in the process. Provider-native web fetching is not affected. This issue is fixed in versions 1.107.7 and 2.52.0. | 14 Oct |
| CVE-2026-107294 | 6.5 medium | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.77.0 until 1.107.2 and 2.24.0, web_fetch_tool, the WebFetch local fallback, and remote FileUrl media downloads buffer the complete HTTP response body before enforcing content-size controls. An attacker-influenced URL can stream an arbitrarily large response that exhausts process memory and crashes the worker; affected media types include ImageUrl, DocumentUrl, VideoUrl, and AudioUrl. SSRF protections remain effective, and the impact is limited to availability. This issue is fixed in versions 1.107.2 and 2.24.0. | 13 Oct |
| CVE-2026-107292 | 6.4 medium | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.34.0 until 2.30.0, the Agent.to_web() and clai web development chat server does not validate the Host header, allowing a website visited by a developer to use DNS rebinding to reach a loopback-hosted agent as a same-origin service. The hostile page can read the served UI and submit chat requests that execute agent tools with the local process's privileges and credentials, causing data disclosure or unwanted side effects. Binding to localhost, Origin checks, and CSRF tokens do not prevent the same-origin DNS rebinding path. This issue is fixed in versions 1.107.5 and 2.30.0. | 14 Oct |
| CVE-2026-114386 | 6.3 medium | 0.00% | no CPE yet | Pydantic AI Harness before 0.59.0 fails to enforce UsageLimits.tool_calls_limit on nested tool calls dispatched from run_code in the CodeMode toolset. Users or prompt-injected content can make the sandboxed program invoke tools via run_nested_call() without check_before_tool_call, exceeding operator limits on costly or side-effecting tools. | 22 Oct |
| CVE-2026-44520 | 5.7 medium | 0.00% | no CPE yet | Docling-Graph turns documents into validated Pydantic objects, then builds a directed knowledge graph with explicit semantic relationships. Prior to 1.5.1, the URLInputHandler class in docling_graph/core/input/handlers.py makes HTTP requests to user-supplied URLs without validating whether the target resolves to a private, loopback, or link-local IP address. The URLValidator only checks for a valid scheme and non-empty netloc, performing no IP-level validation. Additionally, requests.head() was called with allow_redirects=True, allowing an attacker to redirect requests to internal endpoints via an intermediary URL. An attacker who can control the --source CLI argument or PipelineConfig.source API parameter can trigger Server-Side Request Forgery (SSRF). This vulnerability is fixed in 1.5.1. | Mon 26 Oct |
| CVE-2026-58203 | 5.3 medium | 0.00% | pydantic, pydantic-settings | pydantic-settings provides settings management using Pydantic. From 2.12.0 until 2.14.2, NestedSecretsSettingsSource reads secret values from files in a configured secrets_dir. When secrets_nested_subdir=True, a directory entry inside secrets_dir that is a symbolic link pointing outside secrets_dir is followed, so files outside the configured directory are read into settings values. The same code path bypasses the documented secrets_dir_max_size protection. An attacker or lower-privileged component able to influence entries in the configured secrets directory (for example, a writable or shared secrets mount) can turn this into an unintended local file read into settings and can defeat the advertised loading-size cap. This vulnerability is fixed in 2.14.2. | Sun 25 Oct |
| CVE-2026-107288 | 3.7 low | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.77.0 until 1.107.6 and 2.44.0, the local web_fetch_tool and the WebFetch local fallback compare blocked_domains entries with a URL hostname before both values are normalized to the form used by getaddrinfo. An attacker-influenced model can use an equivalent IDNA spelling, non-ASCII label separator, case variation, or trailing root label that resolves to a blocked host but does not match the configured string, causing the application to fetch that host with its own privileges. allowed_domains fails closed for unmatched spellings, and private-IP and cloud-metadata protections remain effective. This issue is fixed in versions 1.107.6 and 2.44.0. | 13 Oct |
| CVE-2026-107293 | 2.3 low | 0.00% | pydantic, pydantic_ai | Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 0.3.4 until 1.107.4 and 2.27.1, OpenTelemetry instrumentation configured with InstrumentationSettings(include_content=False) can export retry prompts outside tool calls in gen_ai.input.messages and pydantic_ai.all_messages. Agents using NativeOutput, PromptedOutput, or output validators on text output can therefore disclose validation feedback, including invalid model values quoted by that feedback, to readers of the telemetry backend. Tool-call retries and deployments that do not use include_content=False are not affected by this specific path. This issue is fixed in versions 1.107.4 and 2.27.1. | 13 Oct |
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EPSS scores: 2026-10-29. NVD cursor: 2026-10-29 00:22. The News page shows only notable CVEs (KEV, your stack, new criticals, EPSS over 50%, or active exploitation per SSVC); this table has all of them.