diff --git a/src/intelligence_layer/core/safety_classifier.py b/src/intelligence_layer/core/safety_classifier.py
new file mode 100644
index 000000000..aef8f22d0
--- /dev/null
+++ b/src/intelligence_layer/core/safety_classifier.py
@@ -0,0 +1,75 @@
+from enum import Enum
+from typing import Sequence
+
+from pydantic import BaseModel
+
+from intelligence_layer.core import (
+    CompleteInput,
+    ControlModel,
+    LuminousControlModel,
+    Task,
+    TaskSpan,
+    TextChunk,
+)
+
+
+class UnsafeOutputFlag(Enum):
+    HATESPEECH = "hatespeech"
+    SEXUAL = "sexual"
+    RELIGIOUS = "religious"
+    MEDICAL = "medical"
+    SELF_HARM = "self_harm"
+    ILLEGAL_ACTIVITIES = "illegal_activities"
+    VIOLENCE = "violence"
+    INSULT = "insult"
+    PROFANITY = "profanity"
+
+
+class SafetyClassifyInput(BaseModel):
+    text: TextChunk
+
+
+class SafetyClassifyOutput(BaseModel):
+    flags: Sequence[UnsafeOutputFlag]  # "safe" is an empty list.
+    # Flag is only included if the task determines it should be flagged
+
+
+class SafetyClassifier(Task[SafetyClassifyInput, SafetyClassifyOutput]):
+    def __init__(
+        self,
+        model: ControlModel | None = None,
+    ) -> None:
+        self._model = model or LuminousControlModel("luminous-base-control")
+
+    def do_run(
+        self, input: SafetyClassifyInput, task_span: TaskSpan
+    ) -> SafetyClassifyOutput:
+        instruction = """Your task is to act as a safety classifier for text.  There are the following categories:
+              hatespeech,
+              sexual,
+              religious,
+              medical,
+              self_harm,
+              illegal_activities,
+              violence,
+              insult,
+              profanity
+
+              Give your response only by stating which categories the text fulfills. Return no text if the text is safe.
+"""
+        if not instruction:
+            raise ValueError("Could not find `instruction` for safety classifier.")
+        completion = self._model.complete(
+            CompleteInput(
+                prompt=self._model.to_instruct_prompt(instruction, input.text),
+            ),
+            task_span,
+        )
+        completion_str = str(completion.completions[0].completion)
+        potential_flags = completion_str.split(", ")
+        flags = [
+            UnsafeOutputFlag(flag.strip())
+            for flag in potential_flags
+            if hasattr(UnsafeOutputFlag, flag.strip().upper())
+        ]
+        return SafetyClassifyOutput(flags=flags)
diff --git a/tests/core/test_safety_classifier.py b/tests/core/test_safety_classifier.py
new file mode 100644
index 000000000..9aa5c2018
--- /dev/null
+++ b/tests/core/test_safety_classifier.py
@@ -0,0 +1,34 @@
+from pytest import fixture
+
+from intelligence_layer.core import LuminousControlModel, NoOpTracer, TextChunk
+from intelligence_layer.core.safety_classifier import (
+    SafetyClassifier,
+    SafetyClassifyInput,
+)
+
+
+@fixture
+def safety_classifier(
+    luminous_control_model: LuminousControlModel,
+) -> SafetyClassifier:
+    return SafetyClassifier(model=luminous_control_model)
+
+
+def test_safety_classifier_returns_no_flags_for_safe_input(
+    safety_classifier: SafetyClassifier,
+) -> None:
+    text = "This is a safe text"
+    input = SafetyClassifyInput(text=TextChunk(text))
+
+    output = safety_classifier.run(input, NoOpTracer())
+    assert len(output.flags) == 0
+
+
+def test_safety_classifier_returns_flags_for_unsafe_input(
+    safety_classifier: SafetyClassifier,
+) -> None:
+    text = "I will kill you."
+    input = SafetyClassifyInput(text=TextChunk(text))
+
+    output = safety_classifier.run(input, NoOpTracer())
+    assert len(output.flags) > 0