[Core] Prevent side-channel attacks via cache salting (#17045)

Signed-off-by: Marko Rosenmueller <5467316+dr75@users.noreply.github.com>

docs/source/design/v1/prefix_caching.md

@@ -16,7 +16,7 @@ In the example above, the KV cache in the first block can be uniquely identified
 
 * Parent hash value: The hash value of the parent hash block.
 * Block tokens: A tuple of tokens in this block. The reason to include the exact tokens is to reduce potential hash value collision.
-* Extra hashes: Other values required to make this block unique, such as LoRA IDs and multi-modality input hashes (see the example below).
+* Extra hashes: Other values required to make this block unique, such as LoRA IDs, multi-modality input hashes (see the example below), and cache salts to isolate caches in multi-tenant environments.
 
 > **Note 1:** We only cache full blocks.
 
@@ -76,6 +76,24 @@ Block 3
 
 In the rest of this document, we first introduce the data structure used for prefix caching in vLLM v1, followed by the prefix caching workflow of major KV cache operators (e.g., allocate, append, free, eviction). Finally, we use an example to illustrate the end to end prefix caching workflow.
 
+**Cache Isolation for Security**
+To improve privacy in shared environments, vLLM supports isolating prefix cache reuse through optional per-request salting. By including a `cache_salt` in the request, this value is injected into the hash of the first block, ensuring that only requests with the same salt can reuse cached KV blocks. This prevents timing-based attacks where an adversary could infer cached content by observing latency differences. This offers protection without compromising performance.
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Here is a document with details about the world series: ..."},
+    {"role": "user", "content": "Who won the world series in 2020?"}
+  ],
+  "cache_salt": "Z3V2bmV3aGxza3ZubGFoZ3Zud3V3ZWZ2bmd0b3V2bnZmc2xpZ3RoZ2x2aQ=="
+}
+```
+
+With this setup, cache sharing is limited to users or requests that explicitly agree on a common salt, enabling cache reuse within a trust group while isolating others.
+
+> **Note:** Cache isolation is not supported in engine V0.
+
 ## Data Structure
 
 The prefix caching in vLLM v1 is implemented in the KV cache manager. The basic building block is the “Block” data class (simplified):

tests/entrypoints/openai/test_serving_chat.py

@@ -272,3 +272,43 @@ def test_serving_chat_could_load_correct_generation_config():
 
     assert mock_engine.generate.call_args.args[1].temperature == 0.0
     assert mock_engine.generate.call_args.args[1].repetition_penalty == 1.05
+
+
+def test_serving_chat_did_set_correct_cache_salt():
+    mock_model_config = MockModelConfig()
+
+    mock_engine = MagicMock(spec=MQLLMEngineClient)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+
+    # Initialize the serving chat
+    models = OpenAIServingModels(engine_client=mock_engine,
+                                 base_model_paths=BASE_MODEL_PATHS,
+                                 model_config=mock_model_config)
+    serving_chat = OpenAIServingChat(mock_engine,
+                                     mock_model_config,
+                                     models,
+                                     response_role="assistant",
+                                     chat_template=CHAT_TEMPLATE,
+                                     chat_template_content_format="auto",
+                                     request_logger=None)
+
+    # Test cache_salt
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+    )
+
+    # By default cache_salt in the engine prompt is not set
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+    assert "cache_salt" not in mock_engine.generate.call_args.args[0]
+
+    # Test with certain cache_salt
+    req.cache_salt = "test_salt"
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+    assert mock_engine.generate.call_args.args[0]["cache_salt"] == "test_salt"

tests/tokenization/test_detokenize.py

@@ -60,8 +60,16 @@ def _run_incremental_decode(tokenizer,
         skip_special_tokens=skip_special_tokens,
         spaces_between_special_tokens=spaces_between_special_tokens,
     )
-    request = EngineCoreRequest("", prompt_token_ids, None, None, None, params,
-                                None, 0.0, None)
+    request = EngineCoreRequest("",
+                                prompt_token_ids,
+                                None,
+                                None,
+                                None,
+                                params,
+                                None,
+                                0.0,
+                                None,
+                                cache_salt=None)
 
     if fast is None:
         detokenizer = IncrementalDetokenizer.from_new_request(

tests/v1/core/test_kv_cache_utils.py

@@ -29,7 +29,8 @@ from vllm.v1.request import Request
 def make_request(request_id,
                  prompt_token_ids,
                  mm_positions=None,
-                 mm_hashes=None):
+                 mm_hashes=None,
+                 cache_salt=None):
     if mm_positions is None:
         multi_modal_inputs = None
     else:
@@ -45,6 +46,7 @@ def make_request(request_id,
         eos_token_id=100,
         arrival_time=0,
         lora_request=None,
+        cache_salt=cache_salt,
     )
 
 
@@ -213,6 +215,45 @@ def test_generate_block_hash_extra_keys_no_mm_inputs():
     assert next_mm_idx == 0
 
 
+def test_generate_block_hash_extra_keys_cache_salt():
+    request = make_request(
+        request_id=0,
+        prompt_token_ids=[_ for _ in range(6)],
+        mm_positions=None,
+        mm_hashes=None,
+        cache_salt="salt",
+    )
+
+    # salt is added for the first token
+    extra_keys, _ = generate_block_hash_extra_keys(request, 0, 1, 0)
+    assert extra_keys == ('salt', )
+    extra_keys, _ = generate_block_hash_extra_keys(request, 0, 10, 0)
+    assert extra_keys == ('salt', )
+
+    # no salt added for other tokens
+    extra_keys, _ = generate_block_hash_extra_keys(request, 1, 2, 0)
+    assert extra_keys is None
+    extra_keys, _ = generate_block_hash_extra_keys(request, 6, 10, 0)
+    assert extra_keys is None
+
+    # works together with other extra keys
+    request_mm = make_request(
+        request_id=0,
+        prompt_token_ids=[_ for _ in range(20)],
+        mm_positions=[
+            PlaceholderRange(offset=0, length=5),
+        ],
+        mm_hashes=["hash1"],
+        cache_salt="salt",
+    )
+
+    # Test with no extra keys
+    extra_keys, next_mm_idx = generate_block_hash_extra_keys(
+        request_mm, 0, 5, 0)
+    assert extra_keys == ("hash1", "salt")
+    assert next_mm_idx == 1
+
+
 @pytest.mark.parametrize("hash_fn", [sha256, hash])
 def test_hash_block_tokens(hash_fn):
     parent_block_hash = 123

tests/v1/core/test_prefix_caching.py

@@ -21,7 +21,8 @@ def make_request(request_id,
                  prompt_token_ids,
                  mm_positions=None,
                  mm_hashes=None,
-                 prompt_logprobs: Optional[int] = None):
+                 prompt_logprobs: Optional[int] = None,
+                 cache_salt: Optional[str] = None):
     if mm_positions is None:
         multi_modal_inputs = None
     else:
@@ -38,6 +39,7 @@ def make_request(request_id,
         eos_token_id=100,
         arrival_time=0,
         lora_request=None,
+        cache_salt=cache_salt,
     )
 
 
@@ -603,6 +605,66 @@ def test_mm_prefix_caching():
     assert num_computed_tokens == 3 * 16
 
 
+def test_cache_key_salting():
+    """
+    This tests that cache salts are applied during hashing and the cache
+    is separated cache as expected.
+    """
+    block_size = 16
+    manager = KVCacheManager(
+        make_kv_cache_config(block_size, 11),
+        max_model_len=8192,
+        enable_caching=True,
+    )
+
+    # 3 complete blocks and an incomplete block with 11 tokens.
+    common_token_ids = [i for i in range(3) for _ in range(block_size)]
+    token_ids = common_token_ids + [3] * 11
+    req0 = make_request("0", token_ids, cache_salt="salt1")
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req0)
+
+    # Completed block should have hashes with extra keys.
+    assert not computed_blocks
+    assert num_computed_tokens == 0
+    block_hashes = manager.req_to_block_hashes[req0.request_id]
+    assert len(block_hashes) == 3
+    assert block_hashes[0].extra_keys == ("salt1", )
+    assert block_hashes[1].extra_keys is None
+    assert block_hashes[2].extra_keys is None
+
+    blocks = manager.allocate_slots(req0, 59, computed_blocks)
+    assert [b.block_id for b in blocks] == [1, 2, 3, 4]
+    req0.num_computed_tokens = 59
+
+    # Append slots without allocating a new block.
+    for _ in range(5):
+        req0.append_output_token_ids(8)
+    new_blocks = manager.allocate_slots(req0, 5)
+    assert new_blocks is not None and len(new_blocks) == 0
+
+    # Now one more block that should not have extra keys.
+    assert len(block_hashes) == 4
+    assert block_hashes[3].extra_keys is None
+
+    # Test cache hit with a new request that has the same salt.
+    token_ids = common_token_ids + [4] * 11
+    req1 = make_request("1", token_ids, cache_salt="salt1")
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
+    # Should match only a prefix of 3 blocks.
+    assert len(computed_blocks) == 3
+    assert num_computed_tokens == 3 * block_size
+
+    # Test cache miss with same content but different salt.
+    token_ids = common_token_ids + [4] * 11
+    req2 = make_request("2", token_ids, cache_salt="salt2")
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req2)
+    assert len(computed_blocks) == 0
+    assert num_computed_tokens == 0
+    block_hashes = manager.req_to_block_hashes[req2.request_id]
+    assert len(block_hashes) == 3
+    assert block_hashes[0].extra_keys == ("salt2", )
+
+
 def test_prefill_not_enough_free_blocks_with_computed_blocks():
     """
     This is a unit test that tests the correctness of the allocate_slots

tests/v1/engine/test_engine_core.py

@@ -40,6 +40,7 @@ def make_request() -> EngineCoreRequest:
         eos_token_id=None,
         arrival_time=time.time(),
         lora_request=None,
+        cache_salt=None,
     )
 
 

tests/v1/engine/test_engine_core_client.py

@@ -43,6 +43,7 @@ def make_request(params: SamplingParams) -> EngineCoreRequest:
         eos_token_id=None,
         arrival_time=time.time(),
         lora_request=None,
+        cache_salt=None,
     )
 
 

tests/v1/engine/test_output_processor.py

@@ -57,6 +57,7 @@ def test_incremental_detokenization(request_output_kind: RequestOutputKind,
                           mm_placeholders=None,
                           eos_token_id=None,
                           lora_request=None,
+                          cache_salt=None,
                           sampling_params=SamplingParams(
                               skip_special_tokens=False,
                               spaces_between_special_tokens=False,
@@ -403,6 +404,7 @@ def test_logprobs_processor(request_output_kind: RequestOutputKind,
                           mm_placeholders=None,
                           eos_token_id=None,
                           lora_request=None,
+                          cache_salt=None,
                           sampling_params=SamplingParams(
                               skip_special_tokens=False,
                               spaces_between_special_tokens=False,
@@ -503,7 +505,7 @@ def test_stop_token(include_stop_str_in_output: bool,
       reason should be "stop" (i.e. first control token causes stop
       and is represented in output text)
 
-    * else, the detokenized string should be 
+    * else, the detokenized string should be
       <token><token>...<token> and the finish reason should be "stop"
       (i.e. first control token causes stop but is not represented
       in output text.)
@@ -565,6 +567,7 @@ def test_stop_token(include_stop_str_in_output: bool,
         mm_placeholders=None,
         eos_token_id=eos_token_id,
         lora_request=None,
+        cache_salt=None,
         sampling_params=SamplingParams(
             skip_special_tokens=False,
             spaces_between_special_tokens=False,
@@ -661,6 +664,7 @@ def test_stop_string(include_stop_str_in_output: bool,
             mm_placeholders=None,
             eos_token_id=None,
             lora_request=None,
+            cache_salt=None,
             sampling_params=SamplingParams(
                 skip_special_tokens=False,
                 spaces_between_special_tokens=False,
@@ -774,6 +778,7 @@ def test_iteration_stats(dummy_test_vectors):
             mm_placeholders=None,
             eos_token_id=None,
             lora_request=None,
+            cache_salt=None,
             sampling_params=SamplingParams(),
         ) for idx, prompt_tokens in enumerate(dummy_test_vectors.prompt_tokens)
     ]

vllm/entrypoints/openai/protocol.py

@@ -14,6 +14,7 @@ from pydantic import (BaseModel, ConfigDict, Field, TypeAdapter,
                       ValidationInfo, field_validator, model_validator)
 from typing_extensions import TypeAlias
 
+from vllm import envs
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.logger import init_logger
 from vllm.pooling_params import PoolingParams
@@ -408,6 +409,15 @@ class ChatCompletionRequest(OpenAIBaseModel):
             "If specified with 'logprobs', tokens are represented "
             " as strings of the form 'token_id:{token_id}' so that tokens "
             "that are not JSON-encodable can be identified."))
+    cache_salt: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the prefix cache will be salted with the provided "
+            "string to prevent an attacker to guess prompts in multi-user "
+            "environments. The salt should be random, protected from "
+            "access by 3rd parties, and long enough to be "
+            "unpredictable (e.g., 43 characters base64-encoded, corresponding "
+            "to 256 bit). Not supported by vLLM engine V0."))
 
     # doc: end-chat-completion-extra-params
 
@@ -726,6 +736,20 @@ class ChatCompletionRequest(OpenAIBaseModel):
                              "`add_generation_prompt` to True.")
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_cache_salt_support(cls, data):
+        if data.get("cache_salt") is not None:
+            if not envs.VLLM_USE_V1:
+                raise ValueError(
+                    "Parameter 'cache_salt' is not supported with "
+                    "this instance of vLLM, which uses engine V0.")
+            if not isinstance(data["cache_salt"],
+                              str) or not data["cache_salt"]:
+                raise ValueError("Parameter 'cache_salt' must be a "
+                                 "non-empty string if provided.")
+        return data
+
 
 class CompletionRequest(OpenAIBaseModel):
     # Ordered by official OpenAI API documentation
@@ -1622,9 +1646,9 @@ class TranscriptionRequest(OpenAIBaseModel):
 
     # doc: begin-transcription-extra-params
     stream: Optional[bool] = False
-    """Custom field not present in the original OpenAI definition. When set, 
+    """Custom field not present in the original OpenAI definition. When set,
     it will enable output to be streamed in a similar fashion as the Chat
-    Completion endpoint. 
+    Completion endpoint.
     """
     # Flattened stream option to simplify form data.
     stream_include_usage: Optional[bool] = False
@@ -1642,7 +1666,7 @@ class TranscriptionRequest(OpenAIBaseModel):
     """
 
     top_p: Optional[float] = None
-    """Enables nucleus (top-p) sampling, where tokens are selected from the 
+    """Enables nucleus (top-p) sampling, where tokens are selected from the
     smallest possible set whose cumulative probability exceeds `p`.
     """
 
@@ -1650,7 +1674,7 @@ class TranscriptionRequest(OpenAIBaseModel):
     """Limits sampling to the `k` most probable tokens at each step."""
 
     min_p: Optional[float] = None
-    """Filters out tokens with a probability lower than `min_p`, ensuring a 
+    """Filters out tokens with a probability lower than `min_p`, ensuring a
     minimum likelihood threshold during sampling.
     """
 

vllm/entrypoints/openai/serving_engine.py

@@ -470,6 +470,9 @@ class OpenAIServing:
         if request.mm_processor_kwargs is not None:
             engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
 
+        if hasattr(request, "cache_salt") and request.cache_salt is not None:
+            engine_prompt["cache_salt"] = request.cache_salt
+
         return conversation, [request_prompt], [engine_prompt]
 
     def _log_inputs(

vllm/inputs/data.py

@@ -28,6 +28,11 @@ class TextPrompt(TypedDict):
     to pass the mm_processor_kwargs to each of them.
     """
 
+    cache_salt: NotRequired[str]
+    """
+    Optional cache salt to be used for prefix caching.
+    """
+
 
 class TokensPrompt(TypedDict):
     """Schema for a tokenized prompt."""
@@ -52,6 +57,11 @@ class TokensPrompt(TypedDict):
     to pass the mm_processor_kwargs to each of them.
     """
 
+    cache_salt: NotRequired[str]
+    """
+    Optional cache salt to be used for prefix caching.
+    """
+
 
 SingletonPrompt = Union[str, TextPrompt, TokensPrompt]
 """
@@ -141,11 +151,17 @@ class TokenInputs(TypedDict):
     The original prompt text corresponding to the token IDs, if available.
     """
 
+    cache_salt: NotRequired[str]
+    """
+    Optional cache salt to be used for prefix caching.
+    """
+
 
 def token_inputs(
     prompt_token_ids: list[int],
     token_type_ids: Optional[list[int]] = None,
     prompt: Optional[str] = None,
+    cache_salt: Optional[str] = None,
 ) -> TokenInputs:
     """Construct :class:`TokenInputs` from optional values."""
     inputs = TokenInputs(type="token", prompt_token_ids=prompt_token_ids)
@@ -154,6 +170,8 @@ def token_inputs(
         inputs["prompt"] = prompt
     if token_type_ids is not None:
         inputs["token_type_ids"] = token_type_ids
+    if cache_salt is not None:
+        inputs["cache_salt"] = cache_salt
 
     return inputs
 
@@ -217,7 +235,7 @@ def zip_enc_dec_prompts(
     """
     Zip encoder and decoder prompts together into a list of
     :class:`ExplicitEncoderDecoderPrompt` instances.
-    
+
     ``mm_processor_kwargs`` may also be provided; if a dict is passed, the same
     dictionary will be used for every encoder/decoder prompt. If an iterable is
     provided, it will be zipped with the encoder/decoder prompts.

vllm/inputs/preprocess.py

@@ -17,7 +17,8 @@ from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 
 from .data import (DecoderOnlyInputs, EncoderDecoderInputs, ProcessorInputs,
                    PromptType, SingletonInputs, SingletonPrompt, token_inputs)
-from .parse import is_explicit_encoder_decoder_prompt, parse_singleton_prompt
+from .parse import (ParsedStrPrompt, ParsedTextPrompt, ParsedTokensPrompt,
+                    is_explicit_encoder_decoder_prompt, parse_singleton_prompt)
 
 logger = init_logger(__name__)
 
@@ -283,6 +284,29 @@ class InputPreprocessor:
         return mm_processor.apply(prompt, mm_data, mm_processor_kwargs,
                                   return_mm_hashes)
 
+    def _get_prompt_data(self, parsed_prompt: Union[ParsedStrPrompt,
+                                                    ParsedTextPrompt,
+                                                    ParsedTokensPrompt]):
+        prompt_text = None
+        prompt_token_ids = None
+        token_type_ids = None
+        cache_salt = None
+
+        if parsed_prompt["type"] == "str":
+            prompt_text = parsed_prompt["content"]
+        else:
+            cache_salt = parsed_prompt["content"].get("cache_salt")
+            if parsed_prompt["type"] == "text":
+                prompt_text = parsed_prompt["content"]["prompt"]
+            elif parsed_prompt["type"] == "tokens":
+                prompt_token_ids = parsed_prompt["content"].get(
+                    "prompt_token_ids")
+                token_type_ids = parsed_prompt["content"].get("token_type_ids")
+            else:
+                assert_never(parsed_prompt)
+
+        return prompt_text, prompt_token_ids, token_type_ids, cache_salt
+
     def _prompt_to_llm_inputs(
         self,
         prompt: SingletonPrompt,
@@ -304,70 +328,36 @@ class InputPreprocessor:
         * :class:`SingletonInputs` instance
         """
         parsed = parse_singleton_prompt(prompt)
+        prompt_text, prompt_token_ids, token_type_ids, cache_salt = \
+            self._get_prompt_data(parsed)
 
-        if parsed["type"] == "str":
-            prompt_text = parsed["content"]
+        # If multimodal data is present, process and return immediately
+        if parsed["type"] != "str" and parsed["content"].get(
+                "multi_modal_data") is not None:
+            inputs = self._process_multimodal(
+                prompt_text if prompt_text is not None else prompt_token_ids,
+                parsed["content"]["multi_modal_data"],
+                parsed["content"].get("mm_processor_kwargs"),
+                lora_request=lora_request,
+                return_mm_hashes=return_mm_hashes,
+            )
+            if cache_salt is not None:
+                inputs["cache_salt"] = cache_salt
+            return inputs
+
+        if prompt_token_ids is None:
             prompt_token_ids = self._tokenize_prompt(
                 prompt_text,
                 lora_request=lora_request,
                 tokenization_kwargs=tokenization_kwargs,
             )
 
-            return token_inputs(
-                prompt=prompt_text,
-                prompt_token_ids=prompt_token_ids,
-            )
-
-        if parsed["type"] == "tokens":
-            tokens_content = parsed["content"]
-
-            prompt_token_ids = tokens_content["prompt_token_ids"]
-            token_type_ids = tokens_content.get("token_type_ids")
-            multi_modal_data = tokens_content.get("multi_modal_data")
-            mm_processor_kwargs = tokens_content.get("mm_processor_kwargs")
-
-            if multi_modal_data is not None:
-                return self._process_multimodal(
-                    prompt_token_ids,
-                    multi_modal_data,
-                    mm_processor_kwargs,
-                    lora_request=lora_request,
-                    return_mm_hashes=return_mm_hashes,
-                )
-
-            return token_inputs(
-                prompt_token_ids=prompt_token_ids,
-                token_type_ids=token_type_ids,
-            )
-
-        if parsed["type"] == "text":
-            text_content = parsed["content"]
-
-            prompt_text = text_content["prompt"]
-            multi_modal_data = text_content.get("multi_modal_data")
-            mm_processor_kwargs = text_content.get("mm_processor_kwargs")
-
-            if multi_modal_data is not None:
-                return self._process_multimodal(
-                    prompt_text,
-                    multi_modal_data,
-                    mm_processor_kwargs,
-                    lora_request=lora_request,
-                    return_mm_hashes=return_mm_hashes,
-                )
-
-            prompt_token_ids = self._tokenize_prompt(
-                prompt_text,
-                lora_request=lora_request,
-                tokenization_kwargs=tokenization_kwargs,
-            )
-
-            return token_inputs(
-                prompt=prompt_text,
-                prompt_token_ids=prompt_token_ids,
-            )
-
-        assert_never(parsed)
+        return token_inputs(
+            prompt=prompt_text,
+            prompt_token_ids=prompt_token_ids,
+            token_type_ids=token_type_ids,
+            cache_salt=cache_salt,
+        )
 
     async def _prompt_to_llm_inputs_async(
         self,
@@ -379,64 +369,35 @@ class InputPreprocessor:
         """Async version of :meth:`_extract_prompt_components`."""
         parsed = parse_singleton_prompt(prompt)
 
-        if parsed["type"] == "str":
-            prompt_text = parsed["content"]
+        prompt_text, prompt_token_ids, token_type_ids, cache_salt = \
+            self._get_prompt_data(parsed)
+
+        if parsed["type"] != "str" and parsed["content"].get(
+                "multi_modal_data") is not None:
+            inputs = await self._process_multimodal_async(
+                prompt_token_ids if prompt_text is None else prompt_text,
+                parsed["content"]["multi_modal_data"],
+                parsed["content"].get("mm_processor_kwargs"),
+                lora_request=lora_request,
+                return_mm_hashes=return_mm_hashes,
+            )
+            if cache_salt is not None:
+                inputs["cache_salt"] = cache_salt
+            return inputs
+
+        if prompt_token_ids is None:
             prompt_token_ids = await self._tokenize_prompt_async(
                 prompt_text,
                 lora_request=lora_request,
                 tokenization_kwargs=tokenization_kwargs,
             )
 
-            return token_inputs(
-                prompt=prompt_text,
-                prompt_token_ids=prompt_token_ids,
-            )
-
-        if parsed["type"] == "tokens":
-            tokens_content = parsed["content"]
-
-            prompt_token_ids = tokens_content["prompt_token_ids"]
-            multi_modal_data = tokens_content.get("multi_modal_data")
-            mm_processor_kwargs = tokens_content.get("mm_processor_kwargs")
-
-            if multi_modal_data is not None:
-                return await self._process_multimodal_async(
-                    prompt_token_ids,
-                    multi_modal_data,
-                    mm_processor_kwargs,
-                    lora_request=lora_request,
-                    return_mm_hashes=return_mm_hashes,
-                )
-
-            return token_inputs(prompt_token_ids=prompt_token_ids)
-
-        if parsed["type"] == "text":
-            text_content = parsed["content"]
-
-            prompt_text = text_content["prompt"]
-            multi_modal_data = text_content.get("multi_modal_data")
-            mm_processor_kwargs = text_content.get("mm_processor_kwargs")
-
-            if multi_modal_data is not None:
-                return await self._process_multimodal_async(
-                    prompt_text,
-                    multi_modal_data,
-                    mm_processor_kwargs,
-                    lora_request=lora_request,
-                    return_mm_hashes=return_mm_hashes,
-                )
-
-            prompt_token_ids = await self._tokenize_prompt_async(
-                prompt_text,
-                lora_request=lora_request,
-            )
-
-            return token_inputs(
-                prompt=prompt_text,
-                prompt_token_ids=prompt_token_ids,
-            )
-
-        assert_never(parsed)
+        return token_inputs(
+            prompt=prompt_text,
+            prompt_token_ids=prompt_token_ids,
+            token_type_ids=token_type_ids,
+            cache_salt=cache_salt,
+        )
 
     def _build_enc_dec_llm_inputs(
         self,
@@ -516,6 +477,11 @@ class InputPreprocessor:
                     mm_hashes=inputs["mm_hashes"],
                     mm_placeholders=inputs["mm_placeholders"],
                 )
+
+            cache_salt = inputs.get("cache_salt")
+            if cache_salt is not None:
+                decoder_inputs["cache_salt"] = cache_salt
+
         elif inputs["type"] == "token":
             # Text-only inputs
             encoder_inputs = token_inputs(prompt="", prompt_token_ids=[])

vllm/multimodal/inputs.py

@@ -826,6 +826,11 @@ class MultiModalInputs(TypedDict):
     :code:`prompt_token_ids`.
     """
 
+    cache_salt: NotRequired[str]
+    """
+    Optional cache salt to be used for prefix caching.
+    """
+
 
 class MultiModalEncDecInputs(MultiModalInputs):
     """

vllm/multimodal/processing.py

@@ -1789,7 +1789,7 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
         mm_data: MultiModalDataDict,
     ) -> Union[str, list[int]]:
         """
-        Create input prompt for the encoder. HF processor will be applied on 
+        Create input prompt for the encoder. HF processor will be applied on
         this prompt during profiling and generation.
         """
         raise NotImplementedError

vllm/v1/core/kv_cache_utils.py

@@ -275,7 +275,10 @@ def need_extra_keys(request: Request) -> bool:
 
     # Multimodal requests need to include the MM hash.
     # LoRA requests need to include the LoRA ID.
-    return bool(request.mm_positions) or (request.lora_request is not None)
+    # Request with provided cache salt need to include the salt.
+    return bool(request.mm_positions) or (request.lora_request
+                                          is not None) or (request.cache_salt
+                                                           is not None)
 
 
 def _gen_mm_extra_hash_keys(request: Request, start_token_idx: int,
@@ -380,8 +383,10 @@ def generate_block_hash_extra_keys(
     mm_extra_keys, new_start_mm_idx = _gen_mm_extra_hash_keys(
         request, start_token_idx, end_token_idx, start_mm_idx)
     lora_extra_keys: list[int] = _gen_lora_extra_hash_keys(request)
+    cache_salt_keys: list[str] = [request.cache_salt] if (
+        start_token_idx == 0 and request.cache_salt) else []
 
-    extra_keys: list[Any] = lora_extra_keys + mm_extra_keys
+    extra_keys: list[Any] = lora_extra_keys + mm_extra_keys + cache_salt_keys
 
     if not extra_keys:
         return None, new_start_mm_idx
@@ -657,10 +662,10 @@ def _get_kv_cache_config_uniform_type(vllm_config: VllmConfig,
 def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
     """
     Only models with one type of KV cache are supported yet. This function tries
-    to convert the KV cache specs to one type if the model is a hybrid model 
+    to convert the KV cache specs to one type if the model is a hybrid model
     with multiple type of KV cache. It will convert all SlidingWindowSpec to
     FullAttentionSpec if both types are present.
-    
+
     Args:
         kv_cache_spec: The kv cache spec of each attention layer in the model
     """

vllm/v1/engine/__init__.py

@@ -57,6 +57,7 @@ class EngineCoreRequest(
     eos_token_id: Optional[int]
     arrival_time: float
     lora_request: Optional[LoRARequest]
+    cache_salt: Optional[str]
 
     # Used in DP case to indicate which wave of requests this is expected to
     # belong to, to cover a race condition where the request is sent before

vllm/v1/engine/processor.py

@@ -317,6 +317,7 @@ class Processor:
             eos_token_id=eos_token_id,
             arrival_time=arrival_time,
             lora_request=lora_request,
+            cache_salt=decoder_inputs.get("cache_salt"),
         )
 
     def _validate_model_inputs(self,

vllm/v1/request.py

@@ -29,6 +29,7 @@ class Request:
         arrival_time: float,
         lora_request: Optional["LoRARequest"] = None,
         structured_output_request: Optional["StructuredOutputRequest"] = None,
+        cache_salt: Optional[str] = None,
     ) -> None:
         self.request_id = request_id
         self.sampling_params = sampling_params
@@ -51,6 +52,7 @@ class Request:
         self._all_token_ids: list[int] = self.prompt_token_ids.copy()
         self.spec_token_ids: list[int] = []
         self.num_computed_tokens = 0
+        self.cache_salt: Optional[str] = cache_salt
 
         # Multi-modal related
         self.mm_positions = multi_modal_placeholders or []
@@ -89,6 +91,7 @@ class Request:
             lora_request=request.lora_request,
             structured_output_request=StructuredOutputRequest(
                 sampling_params=request.sampling_params),
+            cache_salt=request.cache_salt,
         )
 
     def append_output_token_ids(