CVE-2026-107286

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.

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.

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.

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.

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.

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.

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.

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.