docs: add known issues section for TRT-LLM backend (#5801)

Signed-off-by: Yuewei Na <nv-yna@users.noreply.github.com>
Signed-off-by: Yuewei Na <248773860+nv-yna@users.noreply.github.com>
Co-authored-by: Yuewei Na <nv-yna@users.noreply.github.com>

docs/backends/trtllm/README.md

@@ -44,6 +44,7 @@ git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
 - [Multimodal Support](#multimodal-support)
 - [Logits Processing](#logits-processing)
 - [Performance Sweep](#performance-sweep)
+- [Known Issues and Mitigations](#known-issues-and-mitigations)
 
 ## Feature Support Matrix
 
@@ -297,3 +298,31 @@ For detailed instructions on running comprehensive performance sweeps across bot
 Dynamo with TensorRT-LLM currently supports integration with the Dynamo KV Block Manager. This integration can significantly reduce time-to-first-token (TTFT) latency, particularly in usage patterns such as multi-turn conversations and repeated long-context requests.
 
 Here is the instruction: [Running KVBM in TensorRT-LLM](./../../../docs/kvbm/trtllm-setup.md) .
+
+## Known Issues and Mitigations
+
+### KV Cache Exhaustion Causing Worker Deadlock (Disaggregated Serving)
+
+**Issue:** In disaggregated serving mode, TensorRT-LLM workers can become stuck and unresponsive after sustained high-load traffic. Once in this state, workers require a pod/process restart to recover.
+
+**Symptoms:**
+- Workers function normally initially but hang after heavy load testing
+- Inference requests get stuck and eventually timeout
+- Logs show warnings: `num_fitting_reqs=0 and fitting_disagg_gen_init_requests is empty, may not have enough kvCache`
+- Error logs may contain: `asyncio.exceptions.InvalidStateError: invalid state`
+
+**Root Cause:** When `max_tokens_in_buffer` in the cache transceiver config is smaller than the maximum input sequence length (ISL) being processed, KV cache exhaustion can occur under heavy load. This causes context transfers to timeout, leaving workers stuck waiting for phantom transfers and entering an irrecoverable deadlock state.
+
+**Mitigation:** Ensure `max_tokens_in_buffer` exceeds your maximum expected input sequence length. Update your engine configuration files (e.g., `prefill.yaml` and `decode.yaml`):
+
+```yaml
+cache_transceiver_config:
+  backend: DEFAULT
+  max_tokens_in_buffer: 65536  # Must exceed max ISL
+```
+
+For example, see `examples/backends/trtllm/engine_configs/gpt-oss-120b/prefill.yaml`.
+
+**Related Issue:** [#4327](https://github.com/ai-dynamo/dynamo/issues/4327)
+
+---