Threats affecting pkg:pypi/pydantic-ai

### Summary The Pydantic AI development web chat UI (`Agent.to_web()`, `clai web`) did not check the content type of requests to its chat endpoint. This allowed a website visited by a developer to submit a request to a chat UI running on that developer's machine, causing the served agent to run and to execute its tools with the privileges and credentials of the local process. Binding the web UI to localhost — the default — does not prevent this, because a page open in the developer's browser can reach the loopback address. ### Impact Applications and developers serving an agent through `Agent.to_web()` or `clai web`. The consequences depend on the tools the served agent exposes, and can include data disclosure as well as unwanted tool side effects. Tools marked `requires_approval=True` were not protected either, because the endpoint trusts approval decisions relayed by the client. ### Mitigation Upgrade to a patched version. The chat endpoint now requires `Content-Type: application/json` and rejects other requests before the request body is parsed and before the agent runs. The bundled chat UI already sends this header; scripts and other non-browser clients that call the endpoint directly may need to be updated to send it. If you cannot upgrade, don't run the web UI while browsing untrusted sites, stop it when you aren't using it, and don't serve an agent with side-effecting tools through it. ### Credits - Thai Son Dinh from VinSOC Labs (R&D)

CVE-2026-107294 is a medium severity vulnerability in pydantic-ai, a Python agent framework for Generative AI applications. Versions from 1.77.0 up to but not including 1.107.2, and from 2.0.0b1 up to but not including 2.24.0, are affected. The issue involves uncontrolled resource consumption due to buffering the entire HTTP response body before applying content-size controls in certain media download components. This can allow an attacker to cause a denial of service by exhausting process memory and crashing the worker. The vulnerability affects media types such as ImageUrl, DocumentUrl, VideoUrl, and AudioUrl. SSRF protections remain effective, limiting the impact to availability. The issue is fixed in versions 1.107.2 and 2.24.0.

### Summary Pydantic AI's OpenTelemetry instrumentation supports `InstrumentationSettings(include_content=False)` to exclude message content — user prompts, model completions, tool call arguments and responses — from exported telemetry, so that agents can be monitored without sending sensitive content to the observability backend. Retry prompts — the feedback messages Pydantic AI sends back to the model when its output fails validation or an output validator raises `ModelRetry` — were not covered by this setting when they were not associated with a tool call, as with structured output modes that don't use tool calls (e.g. `NativeOutput` or `PromptedOutput`) and output validators applied to text output. Their full content was recorded on model request spans even with `include_content=False`. Since validation feedback can quote invalid values from the model's response, message content that the setting was configured to withhold could reach the telemetry backend. ### Details Retry prompts without an associated tool call were serialized into span message attributes (such as `gen_ai.input.messages` and `pydantic_ai.all_messages`) as regular text parts, and this path did not apply the `include_content` check that every other message part applies. Retry prompts tied to a tool call, and all other message content, were redacted correctly. ### Impact This does not grant an attacker any new access to the agent or its data: the content is only visible to whoever can read the exported telemetry. The exposure matters when traces are exported to a destination whose audience is broader or less trusted than the agent's own data — a third-party observability vendor or a shared backend — which is the scenario `include_content=False` exists for. The disclosed content is limited to retry feedback: validation error details, which can quote invalid values from the model's output, or output-validator `ModelRetry` messages. ### Who Is Affected You are affected if you enabled instrumentation with `include_content=False` and your agent can produce retry prompts outside of tool calls: it uses a structured output mode that doesn't rely on tool calls (such as `NativeOutput` or `PromptedOutput`), or output validators on text output. You are not affected if you don't set `include_content=False`, or if all of your agent's retries come from tool calls (including the default tool-based structured output mode), which were redacted correctly. ### Remediation Upgrade to a patched version; retry prompt content now honors `include_content=False` like all other message content. If you rely on this setting, consider reviewing previously exported traces for retry prompt content that should not have been recorded. ### Workaround for Unpatched Versions If you cannot upgrade, scrub or drop the message attributes (`gen_ai.input.messages`, `gen_ai.output.messages`, `pydantic_ai.all_messages`) in your telemetry pipeline (for example with an OpenTelemetry Collector processor), or use tool-based structured output modes, whose retry feedback honors `include_content=False`. ### Credits Reported privately by the University of Sydney Security Research Team (Liyi Zhou, Ziyue Wang, Strick, Maurice, Chenchen Yu), and later independently discovered and reported publicly, with a fix, by @sean-kim05.

CVE-2026-107292 is an origin validation error in pydantic-ai affecting versions from 1.34.0 up to but not including 2.30.0. The vulnerability allows DNS rebinding attacks via the Host header in the Agent.to_web() and clai web chat server, enabling a malicious website to interact with a loopback-hosted agent as a same-origin service. This can lead to unauthorized execution of agent tools with local process privileges, causing data disclosure and unwanted side effects. The issue is fixed in versions 1.107.5 and 2.30.0.

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.

CVE-2026-107290 is a vulnerability in pydantic-ai affecting versions from 1.77.0 up to but not including 1.107.6, and from 2.0.0b1 up to but not including 2.44.0. It involves inefficient regular expression complexity in the local web_fetch_tool and WebFetch local fallback process, which process server-controlled responses with quadratic time complexity in title extraction, whitespace normalization, and ordered-list numbering. This can cause the event loop to stall for an extended period, impacting other agent runs and requests. The issue is fixed in versions 1.107.6 and 2.44.0.

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.

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.

CVE-2026-107287 is a medium severity vulnerability in pydantic-ai affecting versions from 1.77.0 up to but not including 1.107.7, and from 2.0.0b1 up to but not including 2.52.0. The issue involves uncontrolled resource consumption during HTML-to-Markdown conversion of attacker-controlled HTML with deeply nested block elements, causing excessive CPU and memory use. This can delay processing of other tasks. The vulnerability is fixed in versions 1.107.7 and 2.52.0.

> This issue was posted by Codex Desktop using gpt-6.1-sol on behalf of David. ### Summary Applications that wrap a model with `ConcurrencyLimitedModel` or `limit_model_concurrency` can permanently lose shared concurrency capacity when a streamed request releases its slot from a different task than the one that acquired it. This can happen when a stream ends early, and also when a stream is fully consumed using the default `stream_text()` debouncing. In an application that exposes an affected streaming endpoint to network clients and shares a long-lived model limiter across requests, a client can repeatedly start a stream and disconnect. The completed requests retain their slots, eventually preventing subsequent requests that share the limiter from proceeding. Agent-level `max_concurrency` and non-streaming model requests are not affected by this defect. ### Details The built-in limiter uses `anyio.CapacityLimiter`, which associates each acquired slot with its borrowing task. Pydantic AI's streaming lifecycle can acquire the slot on the task consuming the stream and run cleanup on another internal task. The limiter rejects that release, so the slot remains occupied even after the request has ended. Cleanup can raise a `RuntimeError`; a later request on the borrowing task can also fail because that task still holds a slot. Early termination includes stopping iteration, a consumer exception, and cancellation. Fully consuming `stream_text()` with its default `debounce_by=0.1` can also reach the cross-task release path. Fully consumed streams must therefore not be assumed safe. ### Mitigation Upgrade to a patched release of `pydantic-ai` or `pydantic-ai-slim`. If you cannot upgrade yet, use the agent-level `max_concurrency` setting instead of a concurrency-limited model, or avoid streaming runs through a concurrency-limited model.

### Summary A client that can submit message history to a Pydantic AI UI adapter can reference arbitrary files in the application's model-provider or cloud-storage account. The server forwards the reference to the model provider, which fetches it using the server's own credentials, allowing the client to read files it should not have access to. ### Details UI adapters reconstruct file parts from client-submitted message history and forward them to the model provider. File **URL** parts are validated against a scheme allowlist before being forwarded, but `UploadedFile` references — which point to a file by provider file ID or cloud-storage URI (e.g. `s3://…`, `gs://…`) — were forwarded without validation. Because the provider resolves an `UploadedFile` using the server-side identity (IAM role, service account, or provider API key) rather than the client's, a client that crafts message history containing an attacker-chosen `UploadedFile` can cause the server to read objects belonging to its own account or to other tenants, given a referenceable identifier. ### Impact Applications that pass untrusted client-submitted message history to an agent through a UI adapter (such as the Vercel AI adapter). Exploitation requires the attacker to reference a valid file identifier; depending on how the application names objects, such identifiers are not always unguessable. ### Patches Upgrade to `1.106.0` (1.x) or `2.0.0b6` (the 2.x beta line), which validate `UploadedFile` references on client-submitted messages the same way file URLs are validated. ### Workarounds If users cannot upgrade, do not pass untrusted client-submitted message history to the agent, or strip `UploadedFile` parts from incoming messages before running the agent.

## Summary When an application using Pydantic AI opts a URL into `force_download='allow-local'` (which disables the default block on private/internal IPs), the cloud-metadata blocklist could be bypassed by encoding the metadata IP in an IPv6 transition form (IPv4-mapped IPv6, 6to4, or NAT64). Dual-stack and translated networks route the IPv6 wrapper to the underlying IPv4 endpoint, exposing cloud IAM short-term credentials. This is an incomplete fix of [GHSA-2jrp-274c-jhv3](https://github.com/pydantic/pydantic-ai/security/advisories/GHSA-2jrp-274c-jhv3) / [CVE-2026-25580](https://nvd.nist.gov/vuln/detail/CVE-2026-25580). The parent advisory's remediation guaranteed that "cloud metadata endpoints are always blocked, even with `allow-local`." That guarantee did not hold for IPv6-encoded forms of the metadata IPs. ## Severity Same impact metrics as the parent CVE, but materially narrower attack surface (AC:H instead of AC:L), because exploitation requires the application to have opted into `allow-local` on a URL influenced by untrusted input. ## Who Is Affected Applications are affected **only if** they explicitly opt for `FileUrl` (`ImageUrl`, `AudioUrl`, `VideoUrl`, `DocumentUrl`) into `force_download='allow-local'` on a URL that is, or could be, influenced by untrusted input. Applications are **not** affected if they use any of the bundled integrations to ingest user input, because they do not propagate `force_download` from external data: - `Agent.to_web` / `clai web` - `VercelAIAdapter` - `AGUIAdapter` / `Agent.to_ag_ui` Applications that only download from developer-controlled URLs are not affected. ## Remediation Upgrade to `1.99.0` or later. The cloud-metadata and private-IP blocklists now apply to IPv6 transition forms that route to a blocked IPv4 endpoint (IPv4-mapped IPv6, 6to4, and NAT64 well-known prefix). The blocklists have also been extended to cover additional IANA-reserved IPv4 and IPv6 special-purpose ranges. ## Workaround for Unpatched Versions Avoid passing `force_download='allow-local'` on any URL that could be influenced by untrusted input. If developers must, resolve the hostname themselves and validate the result against their own metadata blocklist — including IPv6-encoded forms — before constructing the `FileUrl`. ## Credits Reported by [j0hndo](mailto:dohyun4466@gmail.com).

CVE-2026-65975 is an incorrect authorization vulnerability in pydantic-ai, a Python agent framework for Generative AI applications. In affected versions, certain UI adapters improperly sanitize client-submitted tool calls, allowing a remote client to trigger execution of registered server tools with client-supplied arguments, bypassing model-generated argument checks. This flaw is fixed in versions 1.107.1 and 2.5.0.

## Summary When an application using Pydantic AI opts a URL into `force_download='allow-local'` (which disables the default block on private/internal IPs) **and runs on a network that routes the affected IPv6 transition forms (NAT64- or ISATAP-configured networks)**, the cloud-metadata blocklist could be bypassed by encoding the metadata IP in an IPv6 transition form that the previous fix did not decode — IPv4-compatible IPv6 (`::a.b.c.d`), the NAT64 RFC 8215 local-use prefix (`64:ff9b:1::/48`), operator-chosen NAT64 prefixes, or ISATAP. The IPv6 wrapper is then delivered to the underlying IPv4 metadata endpoint, exposing cloud IAM short-term credentials. **The bypass is exploitable only in environments whose network actually routes these forms** — NAT64-configured networks (IPv6-only or dual-stack-with-NAT64 deployments, including some Kubernetes setups) for the NAT64 variants, or networks with an ISATAP tunnel for ISATAP. A standard dual-stack cloud VM or container does not route them and is not affected in practice. The IPv4-compatible and Teredo variants are deprecated and addressed as defense-in-depth. This is an incomplete fix of [GHSA-cqp8-fcvh-x7r3](https://github.com/pydantic/pydantic-ai/security/advisories/GHSA-cqp8-fcvh-x7r3) / [CVE-2026-46678](https://nvd.nist.gov/vuln/detail/CVE-2026-46678) (itself a follow-up to [CVE-2026-25580](https://nvd.nist.gov/vuln/detail/CVE-2026-25580)). The prior remediation decoded only IPv4-mapped IPv6, 6to4, and the NAT64 well-known prefix; the metadata guarantee did not hold for the remaining transition forms. ## Severity **MEDIUM** — `CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:N/A:N` = **6.8** Same impact metrics and narrow attack surface as the parent advisory (AC:H): exploitation requires the application to have opted into `allow-local` on a URL influenced by untrusted input, and the NAT64/ISATAP variants additionally require the deployment network to route those forms. **CWE-918**: Server-Side Request Forgery (SSRF) ## Affected Versions | Package | Vulnerable | Patched | |---|---|---| | `pydantic-ai` | `>= 1.56.0, < 1.102.0`; `>= 2.0.0b1, < 2.0.0b3` | `1.102.0`; `2.0.0b3` | | `pydantic-ai-slim` | `>= 1.56.0, < 1.102.0`; `>= 2.0.0b1, < 2.0.0b3` | `1.102.0`; `2.0.0b3` | These transition forms have not been decoded since SSRF protection was introduced in `1.56.0`. ## Who Is Affected Users are affected **only if** their application explicitly opts a `FileUrl` (`ImageUrl`, `AudioUrl`, `VideoUrl`, `DocumentUrl`) into `force_download='allow-local'` on a URL that is, or could be, influenced by untrusted input. Beyond that precondition, the affected encodings only reach a metadata endpoint in environments whose network actually routes them. The broadly-routable IPv4-mapped form was addressed in `1.99.0` (CVE-2026-46678); the additional forms addressed here require a **NAT64-configured network** (IPv6-only or dual-stack-with-NAT64 deployments, including some Kubernetes setups) for the NAT64 variants, or an **ISATAP tunnel** for the ISATAP variant. The IPv4-compatible and Teredo forms are deprecated and not routed by modern stacks; they are addressed as defense-in-depth. Most deployments on a standard dual-stack cloud VM or container are therefore not exploitable in practice, but the fix restores the "always blocked" guarantee for the environments that are. Users are **not** affected if they use any of the bundled integrations to ingest user input, because they do not propagate `force_download` from external data: - `Agent.to_web` / `clai web` - `VercelAIAdapter` - `AGUIAdapter` / `Agent.to_ag_ui` Applications that only download from developer-controlled URLs are not affected. ## Remediation Upgrade to `1.102.0` or later (or `2.0.0b3` or later on the 2.0 pre-release line). The cloud-metadata and private-IP blocklists now decode the embedded IPv4 of every standardized IPv6 transition form before evaluating it — IPv4-mapped, IPv4-compatible, 6to4, NAT64 across all prefix lengths (including the RFC 8215 local-use prefix and operator-chosen prefixes), ISATAP, and Teredo. The set of always-blocked cloud metadata/credential endpoints has also been expanded across providers. ## Workaround for Unpatched Versions Avoid passing `force_download='allow-local'` on any URL that could be influenced by untrusted input. If developers must, resolve the hostname themselves and validate the result against their own metadata blocklist — including IPv6 transition forms — before constructing the `FileUrl`. ## Credits Reported by [@SnailSploit](https://snailsploit.com).

Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 0.0.26 to before 1.56.0, aServer-Side Request Forgery (SSRF) vulnerability exists in Pydantic AI's URL download functionality. When applications accept message history from untrusted sources, attackers can include malicious URLs that cause the server to make HTTP requests to internal network resources, potentially accessing internal services or cloud credentials. This vulnerability only affects applications that accept message history from external users. This vulnerability is fixed in 1.56.0.

Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.34.0 to before 1.51.0, a path traversal vulnerability in the Pydantic AI web UI allows an attacker to serve arbitrary JavaScript in the context of the application by crafting a malicious URL. In affected versions, the CDN URL is constructed using a version query parameter from the request URL. This parameter is not validated, allowing path traversal sequences that cause the server to fetch and serve attacker-controlled HTML/JavaScript from an arbitrary source on the same CDN, instead of the legitimate chat UI package. If a victim clicks the link or visits it via an iframe, attacker-controlled code executes in their browser, enabling theft of chat history and other client-side data. This vulnerability only affects applications that use Agent.to_web to serve a chat interface and clai web to serve a chat interface from the CLI. These are typically run locally (on localhost), but may also be deployed on a remote server. This vulnerability is fixed in 1.51.0.