diff --git a/fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_inference_embedding.h b/fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_inference_embedding.h
index f723a14e2b..b2ed9723d8 100644
--- a/fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_inference_embedding.h
+++ b/fbgemm_gpu/src/dram_kv_embedding_cache/dram_kv_inference_embedding.h
@@ -441,15 +441,18 @@ class DramKVInferenceEmbedding {
 
                         // Random starting cursor based on map size for good
                         // entropy
-                        size_t random_start =
-                            folly::Random::rand32(wlmap->size());
+                        size_t map_size = wlmap->size();
+                        size_t random_start = folly::Random::rand32(map_size);
 
                         // Try to find a used block starting from random
                         // position
                         weight_type* block = nullptr;
                         for (int attempts = 0; attempts < 16; ++attempts) {
+                          // Use modulo to prevent overflow beyond map size
+                          size_t block_index =
+                              (random_start + attempts) % map_size;
                           block = pool->template get_block<weight_type>(
-                              random_start + attempts);
+                              block_index);
                           if (block != nullptr) {
                             // Block is used (not null)
                             row_storage_data_ptr =
diff --git a/fbgemm_gpu/test/tbe/dram_kv/dram_kv_inference_test.py b/fbgemm_gpu/test/tbe/dram_kv/dram_kv_inference_test.py
index 796d453681..6ee31f91d4 100644
--- a/fbgemm_gpu/test/tbe/dram_kv/dram_kv_inference_test.py
+++ b/fbgemm_gpu/test/tbe/dram_kv/dram_kv_inference_test.py
@@ -385,3 +385,61 @@ def test_zero_cache_miss_initialization_with_embedding_cache_mode(self) -> None:
                 f"All zero cache miss results should be identical across calls, "
                 f"but results[0] != results[{i}]",
             )
+
+    def test_random_block_index_no_overflow(self) -> None:
+        """Test that random block selection doesn't overflow when random_start + attempts exceeds map size."""
+        num_shards = 4
+        uniform_init_lower: float = -0.01
+        uniform_init_upper: float = 0.01
+
+        # Setup: Create DRAM KV inference cache with small map size
+        kv_embedding_cache = torch.classes.fbgemm.DramKVEmbeddingInferenceWrapper(
+            num_shards,
+            uniform_init_lower,
+            uniform_init_upper,
+            False,  # disable_random_init=False to enable random block selection
+        )
+        kv_embedding_cache.init(
+            [(16, 4, SparseType.FP16.as_int())],
+            16,
+            4,
+            torch.tensor([0, 20], dtype=torch.int64),
+        )
+
+        # Populate cache with a small number of entries (e.g., 10)
+        # This creates a scenario where random_start could be close to map size
+        # and random_start + 16 attempts would exceed the map size
+        setup_indices = torch.arange(0, 10, dtype=torch.int64)
+        setup_weights = torch.randint(1, 255, (10, 16), dtype=torch.uint8)
+        kv_embedding_cache.set_embeddings(setup_indices, setup_weights)
+
+        # Execute: Request many cache misses to trigger random block selection
+        # This exercises the random block selection code path multiple times
+        # with different random starting points
+        miss_indices = torch.tensor(
+            [100, 101, 102, 103, 104, 105, 106, 107, 108, 109], dtype=torch.int64
+        )
+
+        # Run multiple times to increase probability of hitting edge cases
+        # where random_start is near the end of the map
+        for iteration in range(50):
+            try:
+                result = kv_embedding_cache.get_embeddings(miss_indices)
+
+                # Assert: Verify the operation completes without crash or exception
+                # The result should have correct dimensions
+                self.assertEqual(result.size(0), 10)
+                self.assertEqual(result.size(1), 16)
+
+                # All values should be non-zero (randomized from existing blocks)
+                self.assertTrue(
+                    torch.any(result != 0),
+                    f"Expected non-zero randomized data in iteration {iteration}",
+                )
+            except Exception as e:
+                self.fail(
+                    f"Random block selection failed in iteration {iteration} "
+                    f"with small map size (10 entries). This likely indicates an "
+                    f"overflow issue when random_start + attempts exceeds map size. "
+                    f"Error: {e}"
+                )