diff --git a/codex-lens/src/codexlens/semantic/vector_store.py b/codex-lens/src/codexlens/semantic/vector_store.py
index f1f42c9a..0f6d58f0 100644
--- a/codex-lens/src/codexlens/semantic/vector_store.py
+++ b/codex-lens/src/codexlens/semantic/vector_store.py
@@ -791,7 +791,28 @@ class VectorStore:
         # For cosine space: distance = 1 - similarity
         ids, distances = self._ann_index.search(query_vec, effective_top_k)
 
-        if not ids:
+        if ids is None or distances is None:
+            logger.debug(
+                "ANN search returned null results (ids=%s, distances=%s)",
+                ids,
+                distances,
+            )
+            return []
+
+        if len(ids) == 0 or len(distances) == 0:
+            logger.debug(
+                "ANN search returned empty results (ids=%s, distances=%s)",
+                ids,
+                distances,
+            )
+            return []
+
+        if len(ids) != len(distances):
+            logger.warning(
+                "ANN search returned mismatched result lengths (%d ids, %d distances)",
+                len(ids),
+                len(distances),
+            )
             return []
 
         # Convert distances to similarity scores
diff --git a/codex-lens/tests/test_vector_store.py b/codex-lens/tests/test_vector_store.py
index 3b030122..3fbe25a2 100644
--- a/codex-lens/tests/test_vector_store.py
+++ b/codex-lens/tests/test_vector_store.py
@@ -174,3 +174,80 @@ def test_search_similar(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
     assert [r.path for r in results] == ["a.py", "b.py"]
     assert results[0].score == pytest.approx(1.0)
     assert results[1].score == pytest.approx(0.0)
+
+
+def test_search_with_ann_null_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
+    """_search_with_ann should return [] when ANN search returns null results."""
+    monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
+    store = VectorStore(temp_db)
+
+    class DummyAnn:
+        def count(self) -> int:
+            return 1
+
+        def search(self, query_vec: np.ndarray, top_k: int):
+            return None, None
+
+    store._ann_index = DummyAnn()
+
+    results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
+    assert results == []
+
+
+def test_search_with_ann_empty_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
+    """_search_with_ann should return [] when ANN search returns empty results."""
+    monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
+    store = VectorStore(temp_db)
+
+    class DummyAnn:
+        def count(self) -> int:
+            return 1
+
+        def search(self, query_vec: np.ndarray, top_k: int):
+            return [], []
+
+    store._ann_index = DummyAnn()
+
+    results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
+    assert results == []
+
+
+def test_search_with_ann_mismatched_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
+    """_search_with_ann should return [] when ANN search returns mismatched results."""
+    monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
+    store = VectorStore(temp_db)
+
+    class DummyAnn:
+        def count(self) -> int:
+            return 2
+
+        def search(self, query_vec: np.ndarray, top_k: int):
+            return [1, 2], [0.5]
+
+    store._ann_index = DummyAnn()
+
+    results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
+    assert results == []
+
+
+def test_search_with_ann_valid_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
+    """_search_with_ann should return results for valid ANN outputs."""
+    monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
+    store = VectorStore(temp_db)
+
+    chunk = SemanticChunk(content="chunk A", metadata={})
+    chunk.embedding = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    chunk_id = store.add_chunk(chunk, "a.py")
+
+    class DummyAnn:
+        def count(self) -> int:
+            return 1
+
+        def search(self, query_vec: np.ndarray, top_k: int):
+            return [chunk_id], [0.0]
+
+    store._ann_index = DummyAnn()
+
+    results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
+    assert [r.path for r in results] == ["a.py"]
+    assert results[0].score == pytest.approx(1.0)