diff --git a/docs/compliance_audit_plan.md b/docs/compliance_audit_plan.md
index 4f0625b4..dd34df7c 100644
--- a/docs/compliance_audit_plan.md
+++ b/docs/compliance_audit_plan.md
@@ -140,7 +140,7 @@ benchmark from-config
 ### Program flow (output-caching audit / MLPerf TEST04, two phases)
 
 Every decision gate is shown, with the exit code it produces. Exit codes:
-`0` PASS · `1` FAIL · `3` SetupError · `4` ExecutionError · `130` interrupted
+`0` PASS · `1` FAIL · `2` InputValidationError · `3` SetupError · `4` ExecutionError · `130` interrupted
 (during the main run: the audit never starts; during the audit: the perf
 report is already written).
 
@@ -397,13 +397,15 @@ The generic loop never names a specific test:
    or re-scores them) and has `audit=None` to prevent re-entry into `run_audit`. A phase can
    override this per-`AuditRunSpec` via `test_mode` (`ACC`/`BOTH` keeps its accuracy datasets);
    the orchestrator reads `spec.test_mode` rather than hardcoding perf-only. This override is
-   supported but currently unused — every registered audit (TEST04) runs perf-only. If any phase raises
-   (`SetupError` / `ExecutionError`), `run_audit` aborts **without verifying** — a crashed
-   phase must never produce a result. A phase that returns but whose `Report.complete` is
-   `False` (metrics drain timed out, or the run was interrupted → partial stats) is likewise
-   rejected with `ExecutionError` — a result is never certified on partial data. Errors
-   propagate to the standard CLI handler (`main.py`), which maps `SetupError` → exit `3` and
-   `ExecutionError` → exit `4`.
+   supported but currently unused — every registered audit (TEST04) runs perf-only. If any phase
+   raises a typed CLI error (`InputValidationError` / `SetupError` / `ExecutionError`), `run_audit`
+   aborts **without verifying** — a crashed phase must never produce a result. In particular a
+   phase's `setup_benchmark` can raise `InputValidationError` (e.g. `DatasetValidationError` for an
+   unsaltable warmup dataset); it propagates verbatim rather than being recast as `ExecutionError`.
+   A phase that returns but whose `Report.complete` is `False` (metrics drain timed out, or the run
+   was interrupted → partial stats) is likewise rejected with `ExecutionError` — a result is never
+   certified on partial data. Errors propagate to the standard CLI handler (`main.py`), which maps
+   `InputValidationError` → exit `2`, `SetupError` → exit `3`, and `ExecutionError` → exit `4`.
 5. `result = test.verify(runs, cfg)`
 6. Atomically write the result (`tmp → fsync → rename → fsync(parent)`).
 7. Return the typed `AuditResult`. Because `run_benchmark` currently returns `None` and
diff --git a/src/inference_endpoint/commands/audit.py b/src/inference_endpoint/commands/audit.py
index 1b8eddca..b29000ae 100644
--- a/src/inference_endpoint/commands/audit.py
+++ b/src/inference_endpoint/commands/audit.py
@@ -20,10 +20,12 @@
 
 run_audit returns an AuditResult; cli.py maps PASS/FAIL and main.run() maps
 exceptions to process exit codes:
-  0  PASS           — result.passed is True
-  1  FAIL           — result.passed is False (cli.py raises CLIError)
-  3  SetupError     — config invalid for the audit (bad sample count/index)
-  4  ExecutionError — a phase run failed or produced partial data
+  0  PASS                 — result.passed is True
+  1  FAIL                 — result.passed is False (cli.py raises CLIError)
+  2  InputValidationError — a phase rejected user input (e.g. an unsaltable
+                            warmup dataset), propagated verbatim from setup
+  3  SetupError           — config invalid for the audit (bad sample count/index)
+  4  ExecutionError       — a phase run failed or produced partial data
 """
 
 from __future__ import annotations
@@ -35,7 +37,7 @@
 from ..compliance import AuditRunArtifacts, get_audit_test
 from ..compliance.result import AuditResult, write_result
 from ..config.schema import BenchmarkConfig, DatasetType
-from ..exceptions import ExecutionError, SetupError
+from ..exceptions import CLIError, ExecutionError, SetupError
 from .benchmark.execute import (
     BenchmarkResult,
     TestMode,
@@ -65,6 +67,9 @@ def run_audit(config: BenchmarkConfig, base_report_dir: Path) -> AuditResult:
         AuditResult — always returned; caller maps passed/failed to exit code.
 
     Raises:
+        InputValidationError: A phase rejected user input (e.g. setup_benchmark
+            raising DatasetValidationError for an unsaltable warmup dataset);
+            propagated verbatim rather than recast as a phase ExecutionError.
         SetupError: Config invalid for audit (missing audit block, bad sample
             count/index for the dataset).
         ExecutionError: A phase benchmark run failed.
@@ -116,7 +121,11 @@ def run_audit(config: BenchmarkConfig, base_report_dir: Path) -> AuditResult:
                 )
             bench = run_benchmark_async(ctx)
             finalize_benchmark(ctx, bench)
-        except (SetupError, ExecutionError):
+        except CLIError:
+            # Typed CLI errors already carry the right exit code — SetupError (3),
+            # ExecutionError (4), and the InputValidationError (2) that
+            # setup_benchmark raises for an unsaltable warmup dataset. Propagate
+            # them; only genuinely unexpected exceptions become a phase failure.
             raise
         except Exception as exc:
             raise ExecutionError(f"Audit phase '{spec.label}' failed: {exc}") from exc
diff --git a/src/inference_endpoint/commands/benchmark/cli.py b/src/inference_endpoint/commands/benchmark/cli.py
index 0edb4da7..319bb03c 100644
--- a/src/inference_endpoint/commands/benchmark/cli.py
+++ b/src/inference_endpoint/commands/benchmark/cli.py
@@ -38,7 +38,7 @@
 )
 from inference_endpoint.exceptions import (
     CLIError,
-    DatasetValidationError,
+    DatasetParseError,
     InputValidationError,
 )
 
@@ -67,9 +67,9 @@ def _run(
                 f"{'.'.join(str(p) for p in err['loc'])}: {err['msg']}"
                 for err in e.errors()
             )
-            raise DatasetValidationError(f"Invalid --dataset: {msgs}") from e
+            raise DatasetParseError(f"Invalid --dataset: {msgs}") from e
         except ValueError as e:
-            raise DatasetValidationError(f"Invalid --dataset: {e}") from e
+            raise DatasetParseError(f"Invalid --dataset: {e}") from e
     if config.audit is None:
         run_benchmark(config, mode)
         return
diff --git a/src/inference_endpoint/commands/benchmark/execute.py b/src/inference_endpoint/commands/benchmark/execute.py
index 5bf0564e..b292e084 100644
--- a/src/inference_endpoint/commands/benchmark/execute.py
+++ b/src/inference_endpoint/commands/benchmark/execute.py
@@ -398,6 +398,23 @@ def _load_datasets(
         except Exception as e:
             raise SetupError(f"Failed to load dataset: {e}") from e
 
+        # Fail fast on a warmup dataset that salt cannot bust — at load time,
+        # before any worker/aggregator subprocess is spawned. with_salt() runs
+        # the same check later; this earlier call is deliberate (not redundant),
+        # so an invalid dataset aborts before the subprocess fan-out.
+        warmup = config.settings.warmup
+        if warmup.enabled:
+            if warmup.salt:
+                dataloader.validate_saltable()
+            else:
+                logger.warning(
+                    "Warmup is enabled without salt (--warmup-salt): warmup "
+                    "prompts are issued verbatim, so the server may serve the "
+                    "measured phase from a warm KV/prefix cache and understate "
+                    "latency. Enable --warmup-salt on text-'prompt' datasets to "
+                    "bust the cache."
+                )
+
         if perf_cfg.accuracy_config is not None:
             accuracy_config = perf_cfg.accuracy_config
             scorer_cls, extractor_cls = _resolve_accuracy_components(
diff --git a/src/inference_endpoint/config/schema.py b/src/inference_endpoint/config/schema.py
index e9a69e5d..c07802d1 100644
--- a/src/inference_endpoint/config/schema.py
+++ b/src/inference_endpoint/config/schema.py
@@ -764,12 +764,14 @@ class WarmupConfig(BaseModel):
     ] = Field(None, gt=0, description="Warmup request count (None = full dataset once)")
     salt: Annotated[
         bool,
-        cyclopts.Parameter(
-            alias="--warmup-salt",
-            help="Prepend a unique random hex salt to each warmup prompt",
-        ),
+        cyclopts.Parameter(alias="--warmup-salt"),
     ] = Field(
-        True, description="Prepend a unique random hex salt to each warmup prompt"
+        False,
+        description=(
+            "Prepend a unique random hex salt to each warmup prompt. Requires "
+            "text-'prompt' samples; enabling it on a pre-tokenized "
+            "('input_tokens') dataset is a hard error."
+        ),
     )
     drain: Annotated[
         bool,
diff --git a/src/inference_endpoint/config/templates/concurrency_template_full.yaml b/src/inference_endpoint/config/templates/concurrency_template_full.yaml
index 217a762b..77aa7efd 100644
--- a/src/inference_endpoint/config/templates/concurrency_template_full.yaml
+++ b/src/inference_endpoint/config/templates/concurrency_template_full.yaml
@@ -93,7 +93,7 @@ settings:
   warmup:
     enabled: false  # Enable warmup phase before performance run
     n_requests: null  # Warmup request count (None = full dataset once)
-    salt: true  # Prepend a unique random hex salt to each warmup prompt
+    salt: false  # Prepend a unique random hex salt to each warmup prompt. Requires text-'prompt' samples; enabling it on a pre-tokenized ('input_tokens') dataset is a hard error.
     drain: false  # Drain in-flight warmup requests before starting the performance phase
     warmup_random_seed: 42  # RNG seed for warmup scheduling and sample ordering
   profiling:
diff --git a/src/inference_endpoint/config/templates/offline_template_full.yaml b/src/inference_endpoint/config/templates/offline_template_full.yaml
index 58773595..7f36221a 100644
--- a/src/inference_endpoint/config/templates/offline_template_full.yaml
+++ b/src/inference_endpoint/config/templates/offline_template_full.yaml
@@ -93,7 +93,7 @@ settings:
   warmup:
     enabled: false  # Enable warmup phase before performance run
     n_requests: null  # Warmup request count (None = full dataset once)
-    salt: true  # Prepend a unique random hex salt to each warmup prompt
+    salt: false  # Prepend a unique random hex salt to each warmup prompt. Requires text-'prompt' samples; enabling it on a pre-tokenized ('input_tokens') dataset is a hard error.
     drain: false  # Drain in-flight warmup requests before starting the performance phase
     warmup_random_seed: 42  # RNG seed for warmup scheduling and sample ordering
   profiling:
diff --git a/src/inference_endpoint/config/templates/online_template_full.yaml b/src/inference_endpoint/config/templates/online_template_full.yaml
index 95bc8555..02824f54 100644
--- a/src/inference_endpoint/config/templates/online_template_full.yaml
+++ b/src/inference_endpoint/config/templates/online_template_full.yaml
@@ -94,7 +94,7 @@ settings:
   warmup:
     enabled: false  # Enable warmup phase before performance run
     n_requests: null  # Warmup request count (None = full dataset once)
-    salt: true  # Prepend a unique random hex salt to each warmup prompt
+    salt: false  # Prepend a unique random hex salt to each warmup prompt. Requires text-'prompt' samples; enabling it on a pre-tokenized ('input_tokens') dataset is a hard error.
     drain: false  # Drain in-flight warmup requests before starting the performance phase
     warmup_random_seed: 42  # RNG seed for warmup scheduling and sample ordering
   profiling:
diff --git a/src/inference_endpoint/config/utils.py b/src/inference_endpoint/config/utils.py
index fd62d2bb..9faf4a1a 100644
--- a/src/inference_endpoint/config/utils.py
+++ b/src/inference_endpoint/config/utils.py
@@ -119,6 +119,14 @@ def parse_dataset_string(s: str) -> dict[str, object]:
         for seg in segments[:-1]:
             if seg not in target:
                 target[seg] = {}  # type: ignore[index]
+            elif not isinstance(target[seg], dict):
+                # e.g. 'parser=x,parser.prompt=y' — 'parser' is set as both a
+                # scalar and a nested key. Without this, target[seg] would be a
+                # str and the next assignment raises an uncaught TypeError.
+                raise ValueError(
+                    f"Conflicting option '{key}': '{seg}' is set both as a value "
+                    f"and as a nested key. Use one form for '{seg}'."
+                )
             target = target[seg]  # type: ignore[assignment]
         target[segments[-1]] = value  # type: ignore[index]
 
diff --git a/src/inference_endpoint/dataset_manager/dataset.py b/src/inference_endpoint/dataset_manager/dataset.py
index 17901835..4cff1fc3 100644
--- a/src/inference_endpoint/dataset_manager/dataset.py
+++ b/src/inference_endpoint/dataset_manager/dataset.py
@@ -30,6 +30,7 @@
 from datasets import load_dataset, load_from_disk
 
 from ..config.schema import APIType, ModelParams
+from ..exceptions import DatasetValidationError
 from .transforms import (
     ColumnFilter,
     Transform,
@@ -257,6 +258,34 @@ def load_from_huggingface(
     return ds[split].to_pandas()
 
 
+def _check_unsaltable(sample: Any) -> DatasetValidationError.Reason | None:
+    """Return the Reason a sample cannot be salted, or None if it can.
+
+    Salt requires a dict sample with a str 'prompt' and neither 'input_tokens'
+    nor 'messages'. Both are sent to the server ahead of 'prompt' — adapters
+    forward 'input_tokens' verbatim, and the OpenAI chat adapter prefers
+    'messages' over 'prompt' (openai_msgspec_adapter.py) — so a sample carrying
+    either would ship an unsalted payload even after 'prompt' is salted.
+    A list-form 'prompt' (an OpenAI batch / token-ID array, or this project's
+    multimodal content-part convention) is an explicitly unsupported salt path
+    and is rejected with its own reason.
+    """
+    Reason = DatasetValidationError.Reason
+    if not isinstance(sample, dict):
+        return Reason.TYPE_MISMATCH
+    if "input_tokens" in sample:
+        return Reason.INPUT_TOKENS_SHADOWING
+    if "messages" in sample:
+        return Reason.MESSAGES_SHADOWING
+    if "prompt" not in sample:
+        return Reason.PROMPT_MISSING
+    if isinstance(sample["prompt"], list):
+        return Reason.PROMPT_LIST_UNSUPPORTED
+    if not isinstance(sample["prompt"], str):
+        return Reason.PROMPT_TYPE_MISMATCH
+    return None
+
+
 class Dataset:
     """Class for loading and managing benchmark datasets.
 
@@ -440,55 +469,64 @@ def load_sample(self, index: int) -> Any:
             data = self._apply_salt(data)
         return data
 
+    def validate_saltable(self) -> None:
+        """Raise if any loaded sample cannot be salted.
+
+        salt requires a dict sample with a text ('str') 'prompt' and neither
+        'input_tokens' nor 'messages' (adapters send those verbatim / prefer
+        'messages' over 'prompt', so a salted 'prompt' would never reach the
+        server); a list-form 'prompt' (batch / token-IDs / multimodal content
+        parts) is an unsupported salt path and is rejected too. A non-saltable
+        sample is an error, not a silent
+        skip: skipping would leave the KV cache un-busted. Every sample is
+        checked — a single invalid item fails the run, because the seeded warmup
+        subset can draw any index and salt correctness is all-or-nothing. Called
+        before any load is issued — at benchmark setup and again from with_salt().
+
+        Raises:
+            DatasetValidationError: naming the first offending sample. The index
+                is into the loaded, post-transform sample order, not the source
+                file line.
+        """
+        assert self.data is not None, "Dataset not loaded. Call load() first."
+        for i, sample in enumerate(self.data):
+            if reason := _check_unsaltable(sample):
+                raise DatasetValidationError(
+                    reason,
+                    detail=(
+                        f"sample {i} (index into the loaded, post-transform "
+                        f"order); disable salt (--no-warmup-salt / warmup.salt: "
+                        f"false) or use a text-prompt dataset"
+                    ),
+                )
+
     def with_salt(self, rng: random.Random) -> "Dataset":
         """Return a shallow copy of this dataset that salts each load_sample() call.
 
         The returned dataset shares the same loaded data — no re-loading needed.
         Each load_sample() call on the returned dataset prepends a unique hex salt
-        derived from rng to the prompt field, preventing KV-cache reuse.
+        derived from rng to the 'prompt' field, preventing KV-cache reuse.
+
+        Validates every sample first (see validate_saltable): a non-saltable
+        dataset raises here, before any load is issued.
+
+        Raises:
+            DatasetValidationError: if any sample cannot be salted.
         """
+        self.validate_saltable()
         clone = copy.copy(self)
         clone._salt_rng = rng
         return clone
 
-    def _apply_salt(self, data: Any) -> Any:
-        """Prepend a unique salt to the prompt field of a sample dict."""
+    def _apply_salt(self, data: dict[str, Any]) -> dict[str, Any]:
+        """Prepend a unique salt to the 'prompt' field.
+
+        with_salt() has validated every sample, so ``data`` is guaranteed to be a
+        dict with a str 'prompt' and neither 'input_tokens' nor 'messages'.
+        """
         assert self._salt_rng is not None
-        if not isinstance(data, dict):
-            return data
-        if "input_tokens" in data and "prompt" not in data:
-            self.logger.warning(
-                "salt=True: sample has 'input_tokens' but no 'prompt' — "
-                "salt cannot be applied to pre-tokenized input; KV-cache reuse may not be prevented"
-            )
-            return data
-        if "input_tokens" in data and "prompt" in data:
-            self.logger.warning(
-                "salt=True: sample has both 'input_tokens' and 'prompt' — "
-                "salt applied to 'prompt' only; adapters that use 'input_tokens' "
-                "directly will still reuse the KV cache"
-            )
-        if "prompt" not in data:
-            return data
-        prompt = data["prompt"]
         salt = self._salt_rng.randbytes(8).hex()
-        if isinstance(prompt, str):
-            return {**data, "prompt": f"[{salt}] {prompt}"}
-        if isinstance(prompt, list) and prompt:
-            # Find the first text part at any index (image-first prompts place text at index 1+)
-            for i, part in enumerate(prompt):
-                if isinstance(part, dict) and part.get("type") == "text":
-                    salted_parts = [
-                        *prompt[:i],
-                        {**part, "text": f"[{salt}] {part['text']}"},
-                        *prompt[i + 1 :],
-                    ]
-                    return {**data, "prompt": salted_parts}
-            self.logger.warning(
-                "salt=True: multimodal prompt has no text part — "
-                "salt cannot be applied; KV-cache reuse may not be prevented"
-            )
-        return data  # unsupported prompt type — skip salting
+        return {**data, "prompt": f"[{salt}] {data['prompt']}"}
 
     def num_samples(self) -> int:
         assert self.data is not None, "Dataset not loaded. Call load() first."
diff --git a/src/inference_endpoint/exceptions.py b/src/inference_endpoint/exceptions.py
index 86f25a2f..555de511 100644
--- a/src/inference_endpoint/exceptions.py
+++ b/src/inference_endpoint/exceptions.py
@@ -15,6 +15,8 @@
 
 """Custom exceptions for CLI error handling."""
 
+from enum import Enum
+
 
 class CLIError(Exception):
     """Base exception for CLI errors.
@@ -37,7 +39,58 @@ class InputValidationError(CLIError):
 
 
 class DatasetValidationError(InputValidationError):
-    """Invalid --dataset string or dataset configuration."""
+    """A loaded dataset sample fails salt validation.
+
+    The failure category is a ``Reason``; ``detail`` carries the specifics
+    (offending sample index, remediation hint).
+    """
+
+    class Reason(Enum):
+        """Why a sample cannot be salted."""
+
+        TYPE_MISMATCH = "sample is not a dict"
+        INPUT_TOKENS_SHADOWING = (
+            "sample has 'input_tokens'; salt cannot bust a pre-tokenized cache"
+        )
+        MESSAGES_SHADOWING = (
+            "sample has 'messages'; adapters send that verbatim and prefer it "
+            "over 'prompt', so a salted 'prompt' would never reach the server"
+        )
+        PROMPT_MISSING = "sample has no 'prompt' field"
+        PROMPT_TYPE_MISMATCH = "sample 'prompt' is not a str"
+        PROMPT_LIST_UNSUPPORTED = (
+            "sample 'prompt' is a list (OpenAI batch / token-IDs, or this "
+            "project's multimodal content parts); salt supports only a single "
+            "text 'prompt'"
+        )
+
+    def __init__(
+        self,
+        reason: "DatasetValidationError.Reason",
+        detail: str | None = None,
+    ) -> None:
+        self.reason = reason
+        self.detail = detail
+        message = reason.value if detail is None else f"{reason.value}: {detail}"
+        super().__init__(message)
+
+    def __reduce__(
+        self,
+    ) -> tuple[type, tuple["DatasetValidationError.Reason", str | None], dict]:
+        # args holds the formatted message, but __init__ takes (reason, detail);
+        # reconstruct from the structured fields so copy/pickle round-trips. The
+        # third element mirrors CPython's default exception reducer: it restores
+        # __dict__ (and thus __notes__ / any caller-added attribute) as state.
+        return (type(self), (self.reason, self.detail), self.__dict__)
+
+
+class DatasetParseError(InputValidationError):
+    """A --dataset CLI string could not be parsed into a dataset config.
+
+    Distinct from DatasetValidationError: the failure is in the user's input
+    string (bad format / key=value), before any dataset is loaded — so there is
+    no sample index or Reason, just the underlying parse message.
+    """
 
     pass
 
diff --git a/tests/unit/commands/test_benchmark.py b/tests/unit/commands/test_benchmark.py
index ff5f597f..26f2285a 100644
--- a/tests/unit/commands/test_benchmark.py
+++ b/tests/unit/commands/test_benchmark.py
@@ -93,6 +93,7 @@
     SWEBenchScorer,
 )
 from inference_endpoint.exceptions import (
+    DatasetValidationError,
     ExecutionError,
     InputValidationError,
     SetupError,
@@ -406,6 +407,210 @@ def test_dataset_string_coercion(
             assert ds.accuracy_config is not None
             assert ds.accuracy_config.eval_method == acc_eval_method
 
+    @pytest.mark.unit
+    def test_malformed_dataset_string_raises_parse_error(self):
+        """A malformed --dataset string surfaces as DatasetParseError, not a
+        DatasetValidationError with a vestigial Reason. 'badoption' (no '=')
+        raises inside the field validator, which pydantic wraps — exercising the
+        `except ValidationError` arm of _run."""
+        from inference_endpoint.commands.benchmark import cli
+        from inference_endpoint.exceptions import DatasetParseError
+
+        config = OfflineConfig(**_OFFLINE_KWARGS | {"datasets": []})
+        with pytest.raises(DatasetParseError, match="Invalid --dataset"):
+            cli._run(config, ["data.csv,badoption"], TestMode.PERF)
+
+    @pytest.mark.unit
+    def test_dataset_string_raw_valueerror_raises_parse_error(self):
+        """A bare ValueError from with_updates (not wrapped by pydantic) also
+        surfaces as DatasetParseError — covering _run's second `except` arm."""
+        from inference_endpoint.commands.benchmark import cli
+        from inference_endpoint.exceptions import DatasetParseError
+
+        config = MagicMock()
+        config.datasets = []
+        config.with_updates.side_effect = ValueError("raw parse failure")
+        with pytest.raises(DatasetParseError, match="Invalid --dataset"):
+            cli._run(config, ["x.jsonl"], TestMode.PERF)
+
+    @pytest.mark.unit
+    def test_scalar_then_dotted_key_collision_raises_parse_error(self):
+        """A scalar option shadowed by a dotted one (parser=x,parser.prompt=y)
+        must surface as DatasetParseError, not an uncaught TypeError → exit-1
+        traceback. parse_dataset_string raises ValueError, which _run wraps."""
+        from inference_endpoint.commands.benchmark import cli
+        from inference_endpoint.exceptions import DatasetParseError
+
+        config = OfflineConfig(**_OFFLINE_KWARGS | {"datasets": []})
+        with pytest.raises(DatasetParseError, match="Invalid --dataset"):
+            cli._run(config, ["d.jsonl,parser=x,parser.prompt=y"], TestMode.PERF)
+
+
+@pytest.mark.unit
+class TestRunAuditErrorPropagation:
+    """run_audit must not mask a phase's typed CLI error as a generic
+    ExecutionError. An unsaltable warmup makes setup_benchmark raise
+    DatasetValidationError (InputValidationError → exit 2); in the audit.only
+    path that must propagate, not become ExecutionError (exit 4)."""
+
+    def test_phase_input_validation_error_propagates(self, monkeypatch, tmp_path):
+        from inference_endpoint.commands import audit as audit_mod
+        from inference_endpoint.config.schema import DatasetType
+        from inference_endpoint.exceptions import DatasetValidationError
+
+        spec = MagicMock(label="reference", test_mode=TestMode.PERF)
+        fake_test = MagicMock()
+        fake_test.plan_runs.return_value = [spec]
+        monkeypatch.setattr(audit_mod, "get_audit_test", lambda _test: fake_test)
+
+        def _raise_validation(*args, **kwargs):
+            raise DatasetValidationError(
+                DatasetValidationError.Reason.INPUT_TOKENS_SHADOWING, "sample 0"
+            )
+
+        monkeypatch.setattr(audit_mod, "setup_benchmark", _raise_validation)
+
+        config = MagicMock()
+        config.audit = MagicMock()
+        config.datasets = [MagicMock(type=DatasetType.PERFORMANCE)]
+        config.with_updates.return_value = MagicMock()
+
+        # Not recast to ExecutionError — the typed validation error propagates.
+        with pytest.raises(DatasetValidationError):
+            audit_mod.run_audit(config, tmp_path)
+
+
+@pytest.mark.unit
+class TestLoadDatasetsSaltValidation:
+    """_load_datasets validates salt-compatibility at dataset-load time — before
+    any worker/aggregator subprocess is spawned — when warmup salt is enabled.
+    """
+
+    def _config(self, tmp_path: Path, warmup: WarmupConfig) -> OfflineConfig:
+        ds = tmp_path / "perf.jsonl"
+        ds.write_text('{"prompt": "hello world"}\n{"prompt": "second prompt"}\n')
+        return OfflineConfig(
+            endpoint_config={"endpoints": ["http://test:8000"]},
+            model_params={"name": "test-model"},
+            datasets=[{"path": str(ds)}],
+            settings=OfflineSettings(
+                client=HTTPClientConfig(
+                    num_workers=1, warmup_connections=0, max_connections=10
+                ),
+                warmup=warmup,
+            ),
+        )
+
+    @patch.object(Dataset, "validate_saltable")
+    def test_validates_when_warmup_salt_enabled(self, mock_validate, tmp_path):
+        config = self._config(tmp_path, WarmupConfig(enabled=True, salt=True))
+        _load_datasets(config, tmp_path, TestMode.PERF)
+        mock_validate.assert_called_once()
+
+    @patch.object(Dataset, "validate_saltable")
+    def test_skips_validation_when_warmup_disabled(self, mock_validate, tmp_path):
+        config = self._config(tmp_path, WarmupConfig(enabled=False, salt=True))
+        _load_datasets(config, tmp_path, TestMode.PERF)
+        mock_validate.assert_not_called()
+
+    @patch.object(Dataset, "validate_saltable")
+    def test_skips_validation_when_salt_off(self, mock_validate, tmp_path):
+        config = self._config(tmp_path, WarmupConfig(enabled=True, salt=False))
+        _load_datasets(config, tmp_path, TestMode.PERF)
+        mock_validate.assert_not_called()
+
+    def test_warns_when_warmup_enabled_without_salt(self, tmp_path, caplog):
+        """Warmup without salt primes the server cache with verbatim prompts,
+        risking an understated measured phase — surface it as a warning."""
+        config = self._config(tmp_path, WarmupConfig(enabled=True, salt=False))
+        with caplog.at_level(logging.WARNING):
+            _load_datasets(config, tmp_path, TestMode.PERF)
+        assert any(
+            "warmup is enabled without salt" in r.message.lower()
+            for r in caplog.records
+        )
+
+    def test_no_warning_when_warmup_salt_enabled(self, tmp_path, caplog):
+        config = self._config(tmp_path, WarmupConfig(enabled=True, salt=True))
+        with caplog.at_level(logging.WARNING):
+            _load_datasets(config, tmp_path, TestMode.PERF)
+        assert not any(
+            "warmup is enabled without salt" in r.message.lower()
+            for r in caplog.records
+        )
+
+    def test_unsaltable_perf_dataset_raises_before_spawn(self, tmp_path):
+        """Real validation (unpatched) rejects a non-saltable perf dataset at
+        load time — an int 'prompt' cannot be salted."""
+        ds = tmp_path / "perf.jsonl"
+        ds.write_text('{"prompt": 1}\n{"prompt": 2}\n')
+        config = OfflineConfig(
+            endpoint_config={"endpoints": ["http://test:8000"]},
+            model_params={"name": "test-model"},
+            datasets=[{"path": str(ds)}],
+            settings=OfflineSettings(
+                client=HTTPClientConfig(
+                    num_workers=1, warmup_connections=0, max_connections=10
+                ),
+                warmup=WarmupConfig(enabled=True, salt=True),
+            ),
+        )
+        with pytest.raises(DatasetValidationError, match=r"sample 0\b"):
+            _load_datasets(config, tmp_path, TestMode.PERF)
+
+    def test_online_unsaltable_perf_dataset_raises(self, tmp_path):
+        """The salt check is load-pattern agnostic — online runs validate too."""
+        ds = tmp_path / "perf.jsonl"
+        ds.write_text('{"prompt": 1}\n')
+        config = OnlineConfig(
+            endpoint_config={"endpoints": ["http://test:8000"]},
+            model_params={"name": "test-model"},
+            datasets=[{"path": str(ds)}],
+            settings=OnlineSettings(
+                load_pattern=LoadPattern(type=LoadPatternType.POISSON, target_qps=10),
+                client=HTTPClientConfig(
+                    num_workers=1, warmup_connections=0, max_connections=10
+                ),
+                warmup=WarmupConfig(enabled=True, salt=True),
+            ),
+        )
+        with pytest.raises(DatasetValidationError, match=r"sample 0\b"):
+            _load_datasets(config, tmp_path, TestMode.PERF)
+
+    def test_accuracy_only_skips_salt_validation(self, tmp_path):
+        """TestMode.ACC never loads the perf dataset, so an unsaltable perf
+        dataset with warmup salt on must NOT be validated (dataloader is None).
+        Guards against a refactor validating a None dataloader."""
+        perf = tmp_path / "perf.jsonl"
+        perf.write_text('{"prompt": 1}\n')  # unsaltable — would raise if validated
+        fake_acc_df = pd.DataFrame(
+            [{"instance_id": "repo__repo-0", "prompt": "Fix bug 0"}]
+        )
+        config = OfflineConfig(
+            endpoint_config={"endpoints": ["http://test:8000"]},
+            model_params={"name": "test-model"},
+            datasets=[
+                {"type": "performance", "path": str(perf)},
+                {
+                    "name": "swe_bench",
+                    "type": "accuracy",
+                    "accuracy_config": {"eval_method": "swe_bench_scorer"},
+                },
+            ],
+            settings=OfflineSettings(
+                client=HTTPClientConfig(
+                    num_workers=1, warmup_connections=0, max_connections=10
+                ),
+                warmup=WarmupConfig(enabled=True, salt=True),
+            ),
+        )
+        with (
+            patch.object(SWEBenchScorer, "preflight"),
+            patch.object(SWEBench, "generate", return_value=fake_acc_df),
+        ):
+            perf_loader, _ = _load_datasets(config, tmp_path, TestMode.ACC)
+        assert perf_loader is None
+
 
 class TestCommandHandlers:
     """Test offline/online/from_config handlers (mock run_benchmark)."""
@@ -496,6 +701,33 @@ def test_use_legacy_loadgen_qps_metrics_default_and_disable(self):
         lp = bound.arguments["config"].settings.load_pattern
         assert lp.use_legacy_loadgen_qps_metrics is False
 
+    @pytest.mark.unit
+    def test_warmup_salt_flag_default_and_negative(self):
+        """warmup.salt defaults off; --warmup-salt enables it; --no-warmup-salt
+        (the flag the salt-validation remediation message points to) disables
+        it."""
+        base = [
+            "offline",
+            "--endpoints",
+            "http://h:80",
+            "--model",
+            "m",
+            "--dataset",
+            "d.jsonl",
+        ]
+        _, bound, _ = benchmark_app.parse_args(base, exit_on_error=False)
+        assert bound.arguments["config"].settings.warmup.salt is False
+
+        _, bound, _ = benchmark_app.parse_args(
+            [*base, "--warmup-salt"], exit_on_error=False
+        )
+        assert bound.arguments["config"].settings.warmup.salt is True
+
+        _, bound, _ = benchmark_app.parse_args(
+            [*base, "--no-warmup-salt"], exit_on_error=False
+        )
+        assert bound.arguments["config"].settings.warmup.salt is False
+
     @pytest.mark.unit
     def test_loadgen_flag_serialized_only_for_poisson(self):
         """``use_legacy_loadgen_qps_metrics`` is dropped from the serialized
@@ -966,7 +1198,7 @@ def test_defaults(self):
         cfg = WarmupConfig()
         assert cfg.enabled is False
         assert cfg.n_requests is None
-        assert cfg.salt is True
+        assert cfg.salt is False
         assert cfg.drain is False
 
     @pytest.mark.unit
@@ -1776,7 +2008,7 @@ def test_warmup_n_requests_none_when_unset(self, base_rt_settings, simple_datase
         assert phases[0].runtime_settings.n_samples_to_issue is None
 
     @pytest.mark.unit
-    def test_warmup_defaults_uses_salt(self, base_rt_settings, simple_dataset):
+    def test_warmup_defaults_no_salt(self, base_rt_settings, simple_dataset):
         config = OfflineConfig(
             **_OFFLINE_KWARGS,
             settings=OfflineSettings(warmup=WarmupConfig(enabled=True)),
@@ -1784,7 +2016,8 @@ def test_warmup_defaults_uses_salt(self, base_rt_settings, simple_dataset):
         ctx = self._make_ctx(config, base_rt_settings, simple_dataset)
         phases = _build_phases(ctx)
 
-        assert phases[0].dataset._salt_rng is not None
+        assert phases[0].dataset._salt_rng is None
+        assert phases[0].dataset is simple_dataset
 
     @pytest.mark.unit
     def test_warmup_without_salt_uses_raw_dataloader(
diff --git a/tests/unit/dataset_manager/test_salted_dataset.py b/tests/unit/dataset_manager/test_salted_dataset.py
index acff8900..324e89b7 100644
--- a/tests/unit/dataset_manager/test_salted_dataset.py
+++ b/tests/unit/dataset_manager/test_salted_dataset.py
@@ -17,11 +17,11 @@
 
 import random
 import re
-from unittest.mock import MagicMock
 
 import pandas as pd
 import pytest
 from inference_endpoint.dataset_manager.dataset import Dataset
+from inference_endpoint.exceptions import DatasetValidationError
 
 
 def _make_loaded_dataset(rows: list[dict]) -> Dataset:
@@ -31,7 +31,6 @@ def _make_loaded_dataset(rows: list[dict]) -> Dataset:
     ds.transforms = None
     ds.repeats = 1
     ds.data = list(rows)
-    ds.logger = MagicMock()
     ds._salt_rng = None
     return ds
 
@@ -143,77 +142,145 @@ def test_seeded_rng_is_reproducible(self):
 
 
 @pytest.mark.unit
-class TestSaltPassthrough:
-    """Samples without a 'prompt' key, or non-dict samples, are passed through unchanged."""
+class TestSaltValidation:
+    """with_salt() hard-errors up front unless every sample has a text 'prompt'.
 
-    def test_dict_without_prompt_key_is_unchanged(self):
+    salt=True guarantees a KV-cache-busting prefix; a sample it cannot salt is a
+    configuration error, not something to skip silently. Validation runs in
+    with_salt() (before any load is issued), so the error names the offending
+    sample and no partial warmup runs against an unsalted dataset.
+    """
+
+    def test_dict_without_prompt_key_raises(self):
         inner = _make_loaded_dataset([{"question": "what is 2+2?", "answer": "4"}])
-        sd = inner.with_salt(random.Random())
-        assert sd.load_sample(0) == {"question": "what is 2+2?", "answer": "4"}
+        with pytest.raises(DatasetValidationError, match="prompt"):
+            inner.with_salt(random.Random())
 
-    def test_empty_dict_is_unchanged(self):
+    def test_empty_dict_raises(self):
         inner = _make_loaded_dataset([{}])
-        sd = inner.with_salt(random.Random())
-        assert sd.load_sample(0) == {}
+        with pytest.raises(DatasetValidationError, match="prompt"):
+            inner.with_salt(random.Random())
 
-    def test_non_dict_sample_is_returned_as_is(self):
+    def test_non_dict_sample_raises(self):
         inner = _make_loaded_dataset([{"prompt": "x"}])
         inner.data = ["raw string sample"]
-        sd = inner.with_salt(random.Random())
-        assert sd.load_sample(0) == "raw string sample"
-
-    def test_multimodal_list_prompt_first_text_part_is_salted(self):
+        with pytest.raises(DatasetValidationError, match="dict"):
+            inner.with_salt(random.Random())
+
+    def test_list_prompt_raises(self):
+        # A list-form 'prompt' (here, multimodal content parts; also OpenAI
+        # batch / token-ID arrays per the spec) is an explicitly unsupported
+        # salt path — reject with a dedicated, actionable reason, not the
+        # generic non-str message.
         content_parts = [
             {"type": "text", "text": "describe this image"},
             {"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}},
         ]
         inner = _make_loaded_dataset([{"prompt": content_parts}])
-        sd = inner.with_salt(random.Random())
-        parts = sd.load_sample(0)["prompt"]
-        assert isinstance(parts, list)
-        assert len(parts) == 2
-        assert re.match(r"^\[([0-9a-f]{16})\] describe this image$", parts[0]["text"])
-        assert parts[1] == content_parts[1]
-
-    def test_multimodal_image_first_text_at_index_1_is_salted(self):
-        content_parts = [
-            {"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}},
-            {"type": "text", "text": "what do you see?"},
-        ]
-        inner = _make_loaded_dataset([{"prompt": content_parts}])
-        sd = inner.with_salt(random.Random())
-        parts = sd.load_sample(0)["prompt"]
-        assert parts[0] == content_parts[0]
-        assert re.match(r"^\[([0-9a-f]{16})\] what do you see\?$", parts[1]["text"])
-
-    def test_multimodal_list_prompt_original_not_mutated(self):
-        content_parts = [{"type": "text", "text": "original text"}]
-        inner = _make_loaded_dataset([{"prompt": content_parts}])
-        sd = inner.with_salt(random.Random())
-        sd.load_sample(0)
-        assert inner.data[0]["prompt"][0]["text"] == "original text"
+        with pytest.raises(DatasetValidationError) as exc_info:
+            inner.with_salt(random.Random())
+        assert (
+            exc_info.value.reason
+            is DatasetValidationError.Reason.PROMPT_LIST_UNSUPPORTED
+        )
 
-    def test_unknown_prompt_type_is_not_salted(self):
+    def test_non_str_prompt_raises(self):
         inner = _make_loaded_dataset([{"prompt": 42}])
-        sd = inner.with_salt(random.Random())
-        assert sd.load_sample(0) == {"prompt": 42}
+        with pytest.raises(DatasetValidationError, match="str"):
+            inner.with_salt(random.Random())
 
-    def test_input_tokens_only_warns_and_passes_through(self):
+    def test_input_tokens_only_raises(self):
         inner = _make_loaded_dataset([{"input_tokens": [1, 2, 3]}])
-        sd = inner.with_salt(random.Random())
-        result = sd.load_sample(0)
-        assert result == {"input_tokens": [1, 2, 3]}
-        sd.logger.warning.assert_called_once()
-        assert "input_tokens" in sd.logger.warning.call_args[0][0]
+        with pytest.raises(DatasetValidationError, match="input_tokens"):
+            inner.with_salt(random.Random())
 
-    def test_input_tokens_and_prompt_warns_and_salts_prompt(self):
+    def test_agentic_messages_sample_raises_messages_shadowing(self):
+        # Agentic datasets store dict samples keyed by 'messages'. The chat
+        # adapter sends 'messages' verbatim, so salting 'prompt' cannot reach the
+        # server — reject with the shadowing reason.
+        inner = _make_loaded_dataset(
+            [{"messages": [{"role": "user", "content": "hi"}]}]
+        )
+        with pytest.raises(DatasetValidationError) as exc_info:
+            inner.with_salt(random.Random())
+        assert exc_info.value.reason is DatasetValidationError.Reason.MESSAGES_SHADOWING
+
+    def test_messages_and_prompt_raises_messages_shadowing(self):
+        # A sample carrying both 'messages' and a valid str 'prompt' would pass a
+        # prompt-only check, but to_endpoint_request() prefers 'messages' — so
+        # _apply_salt() would salt an ignored field and silently fail to bust the
+        # cache. This must be a hard error, not a valid sample.
+        inner = _make_loaded_dataset(
+            [{"messages": [{"role": "user", "content": "hi"}], "prompt": "hello"}]
+        )
+        with pytest.raises(DatasetValidationError) as exc_info:
+            inner.with_salt(random.Random())
+        assert exc_info.value.reason is DatasetValidationError.Reason.MESSAGES_SHADOWING
+
+    def test_input_tokens_and_prompt_raises(self):
         inner = _make_loaded_dataset([{"input_tokens": [1, 2, 3], "prompt": "hello"}])
+        with pytest.raises(DatasetValidationError, match="input_tokens"):
+            inner.with_salt(random.Random())
+
+    def test_error_names_offending_sample_index(self):
+        inner = _make_loaded_dataset([{"prompt": "ok"}, {"prompt": 42}])
+        with pytest.raises(DatasetValidationError, match=r"sample 1\b"):
+            inner.with_salt(random.Random())
+
+    def test_valid_str_prompt_dataset_does_not_raise(self):
+        inner = _make_loaded_dataset([{"prompt": "a"}, {"prompt": "b"}])
         sd = inner.with_salt(random.Random())
-        result = sd.load_sample(0)
-        assert result["input_tokens"] == [1, 2, 3]
-        assert result["prompt"].startswith("[")
-        sd.logger.warning.assert_called_once()
-        assert "input_tokens" in sd.logger.warning.call_args[0][0]
+        assert sd.load_sample(0)["prompt"].startswith("[")
+
+    def test_data_none_raises_not_loaded(self):
+        inner = _make_loaded_dataset([{"prompt": "x"}])
+        inner.data = None
+        # None means "not loaded" — load() always sets a list. Validating an
+        # unloaded dataset is a programming error, not a silent no-op.
+        with pytest.raises(AssertionError, match="not loaded"):
+            inner.with_salt(random.Random())
+
+    def test_data_empty_list_does_not_raise(self):
+        inner = _make_loaded_dataset([])
+        # Zero samples — no violation, so no error.
+        assert inner.with_salt(random.Random())._salt_rng is not None
+
+    def test_validate_saltable_noop_on_valid(self):
+        inner = _make_loaded_dataset([{"prompt": "a"}, {"prompt": "b"}])
+        assert inner.validate_saltable() is None
+
+    def test_validate_saltable_raises_on_bad_sample(self):
+        inner = _make_loaded_dataset([{"prompt": "ok"}, {"input_tokens": [1, 2]}])
+        with pytest.raises(DatasetValidationError, match="input_tokens"):
+            inner.validate_saltable()
+
+    @pytest.mark.parametrize(
+        "sample, expected_reason",
+        [
+            ("raw string", DatasetValidationError.Reason.TYPE_MISMATCH),
+            (
+                {"input_tokens": [1, 2]},
+                DatasetValidationError.Reason.INPUT_TOKENS_SHADOWING,
+            ),
+            (
+                {"messages": [{"role": "user", "content": "hi"}], "prompt": "hi"},
+                DatasetValidationError.Reason.MESSAGES_SHADOWING,
+            ),
+            ({"question": "?"}, DatasetValidationError.Reason.PROMPT_MISSING),
+            ({"prompt": 42}, DatasetValidationError.Reason.PROMPT_TYPE_MISMATCH),
+            (
+                {"prompt": [{"type": "text", "text": "hi"}]},
+                DatasetValidationError.Reason.PROMPT_LIST_UNSUPPORTED,
+            ),
+        ],
+    )
+    def test_error_exposes_typed_reason(self, sample, expected_reason):
+        inner = _make_loaded_dataset([{"prompt": "ok"}])
+        inner.data = [sample]
+        with pytest.raises(DatasetValidationError) as exc_info:
+            inner.validate_saltable()
+        assert exc_info.value.reason is expected_reason
+        assert exc_info.value.detail is not None
 
 
 @pytest.mark.unit
diff --git a/tests/unit/openai/test_msgspec_adapter.py b/tests/unit/openai/test_msgspec_adapter.py
index 1fac94d2..72e8127f 100644
--- a/tests/unit/openai/test_msgspec_adapter.py
+++ b/tests/unit/openai/test_msgspec_adapter.py
@@ -359,3 +359,27 @@ def test_dataset_transforms_preserve_chat_template_kwargs_dict():
     request = OpenAIMsgspecAdapter.to_endpoint_request(Query(id="q5", data=row))
     payload = json.loads(msgspec.json.encode(request))
     assert payload["chat_template_kwargs"] == chat_template_kwargs
+
+
+@pytest.mark.unit
+def test_messages_shadow_prompt_in_request():
+    """'messages' takes precedence over 'prompt' in the emitted request.
+
+    A sample carrying both fields sends only 'messages'; the 'prompt' is dropped
+    entirely. This is why _check_unsaltable() rejects such samples — salting the
+    ignored 'prompt' would not change the payload and would silently fail to bust
+    the KV cache.
+    """
+    query = Query(
+        id="q6",
+        data={
+            "model": "m",
+            "messages": [{"role": "user", "content": "authoritative"}],
+            "prompt": "[deadbeefdeadbeef] shadowed",
+        },
+    )
+    request = OpenAIMsgspecAdapter.to_endpoint_request(query)
+    payload = json.loads(msgspec.json.encode(request))
+
+    assert [m["content"] for m in payload["messages"]] == ["authoritative"]
+    assert "shadowed" not in json.dumps(payload)
diff --git a/tests/unit/test_exceptions.py b/tests/unit/test_exceptions.py
index 22834e64..d232bda5 100644
--- a/tests/unit/test_exceptions.py
+++ b/tests/unit/test_exceptions.py
@@ -24,8 +24,13 @@
 - Clear error categorization (validation vs setup vs execution)
 """
 
+import copy
+import pickle
+
+import pytest
 from inference_endpoint.exceptions import (
     CLIError,
+    DatasetValidationError,
     ExecutionError,
     InputValidationError,
     SetupError,
@@ -78,3 +83,54 @@ def test_exception_chaining(self):
 
         assert chained.__cause__ is original
         assert isinstance(chained, InputValidationError)
+
+
+class TestDatasetValidationErrorRoundTrip:
+    """DatasetValidationError carries structured fields (reason + detail), so it
+    overrides __reduce__ to stay copy/pickle-safe: args holds only the formatted
+    message, which __init__ cannot consume as (reason, detail)."""
+
+    @pytest.mark.unit
+    @pytest.mark.parametrize(
+        "exc",
+        [
+            DatasetValidationError(
+                DatasetValidationError.Reason.INPUT_TOKENS_SHADOWING
+            ),
+            DatasetValidationError(
+                DatasetValidationError.Reason.PROMPT_LIST_UNSUPPORTED, "sample 3"
+            ),
+        ],
+        ids=["reason-only", "reason-and-detail"],
+    )
+    @pytest.mark.parametrize(
+        # pickle round-trips a self-constructed exception (trusted, not external
+        # input) — this asserts __reduce__ reconstructs it from reason + detail.
+        "clone_fn",
+        [copy.copy, copy.deepcopy, lambda e: pickle.loads(pickle.dumps(e))],
+        ids=["copy", "deepcopy", "pickle"],
+    )
+    def test_round_trip_preserves_reason_detail_message(self, exc, clone_fn):
+        clone = clone_fn(exc)
+        assert clone.reason is exc.reason
+        assert clone.detail == exc.detail
+        assert str(clone) == str(exc)
+
+    @pytest.mark.unit
+    @pytest.mark.parametrize(
+        "clone_fn",
+        [copy.copy, copy.deepcopy, lambda e: pickle.loads(pickle.dumps(e))],
+        ids=["copy", "deepcopy", "pickle"],
+    )
+    def test_round_trip_preserves_notes_and_extra_state(self, clone_fn):
+        # The 3-tuple __reduce__ restores __dict__ as state, so __notes__ (PEP
+        # 678) and any caller-added attribute survive — matching CPython's
+        # default exception reducer.
+        exc = DatasetValidationError(
+            DatasetValidationError.Reason.MESSAGES_SHADOWING, "sample 7"
+        )
+        exc.add_note("diagnostic note")
+        exc.extra_attr = "kept"
+        clone = clone_fn(exc)
+        assert getattr(clone, "__notes__", None) == ["diagnostic note"]
+        assert clone.extra_attr == "kept"