diff --git a/gemma/gm/nn/_modules.py b/gemma/gm/nn/_modules.py
index 085408bd..4306112c 100644
--- a/gemma/gm/nn/_modules.py
+++ b/gemma/gm/nn/_modules.py
@@ -268,6 +268,15 @@ def __call__(
       # [batch_size, seq_len, cache_size]
       attn_mask *= sliding_mask
 
+    # Ensure attention mask cache dimension matches logits cache dimension.
+    # The attn_mask may have been created with a larger cache_length during
+    # prefill, but the actual cache (and thus logits) may have been sliced.
+    # Shape: logits is [batch_size, seq_len, num_heads, cache_size]
+    actual_cache_size = logits.shape[-1]
+    if attn_mask.shape[-1] != actual_cache_size:
+      # Slice the attention mask to match the actual cache size being used.
+      attn_mask = attn_mask[..., :actual_cache_size]
+
     # [batch_size, seq_len, num_heads, cache_size]
     padded_logits = jnp.where((jnp.expand_dims(attn_mask, -2)), logits, K_MASK)
 
diff --git a/gemma/gm/nn/_modules_mask_test.py b/gemma/gm/nn/_modules_mask_test.py
new file mode 100644
index 00000000..48262c43
--- /dev/null
+++ b/gemma/gm/nn/_modules_mask_test.py
@@ -0,0 +1,101 @@
+# Copyright 2025 DeepMind Technologies Limited.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Test for attention mask broadcasting bug (Issue #407)."""
+
+import jax
+from jax import numpy as jnp
+import pytest
+
+
+class TestAttentionMaskBroadcasting:
+  """Tests for attention mask shape compatibility."""
+
+  def test_attention_mask_broadcasting_shape_mismatch(self):
+    """Reproduce ValueError from Issue #407: Incompatible shapes for broadcasting.
+    
+    The bug occurs when attn_mask and logits have mismatched cache dimensions:
+    - attn_mask shape after expand_dims: (B, L, 1, cache_size_1)
+    - logits shape: (B, L, num_heads, cache_size_2)
+    
+    where cache_size_1 != cache_size_2, causing broadcasting failure.
+    """
+    # Simulate the scenario from the error:
+    # attn_mask originally: (1, 1447, 5234) -> after expand_dims: (1, 1447, 1, 5234)
+    # logits: (1, 1447, 8, 4096)
+    batch_size, seq_len, num_heads = 1, 1447, 8
+    mask_cache_size = 5234
+    logits_cache_size = 4096
+    
+    # Original 3D attention mask (before expand_dims)
+    attn_mask = jnp.ones((batch_size, seq_len, mask_cache_size), dtype=bool)
+    
+    # Logits from attention computation
+    logits = jnp.zeros((batch_size, seq_len, num_heads, logits_cache_size))
+    
+    K_MASK = jnp.finfo(logits.dtype).min
+    
+    # This is the line from _modules.py:277 that causes the error
+    with pytest.raises(ValueError, match="Incompatible shapes for broadcasting"):
+      _ = jnp.where((jnp.expand_dims(attn_mask, -2)), logits, K_MASK)
+
+  def test_attention_mask_broadcasting_with_auto_slice_fix(self):
+    """Test that automatic slicing fixes the shape mismatch (Issue #407 fix)."""
+    batch_size, seq_len, num_heads = 1, 1447, 8
+    mask_cache_size = 5234
+    logits_cache_size = 4096
+    
+    # Original 3D attention mask (larger than needed)
+    attn_mask = jnp.ones((batch_size, seq_len, mask_cache_size), dtype=bool)
+    
+    # Logits from attention computation
+    logits = jax.random.normal(
+        jax.random.PRNGKey(0), (batch_size, seq_len, num_heads, logits_cache_size)
+    )
+    
+    K_MASK = jnp.finfo(logits.dtype).min
+    
+    # Apply the fix: slice attention mask to match logits cache size
+    actual_cache_size = logits.shape[-1]
+    if attn_mask.shape[-1] != actual_cache_size:
+      attn_mask = attn_mask[..., :actual_cache_size]
+    
+    # Now this should work without errors
+    padded_logits = jnp.where(
+        (jnp.expand_dims(attn_mask, -2)), logits, K_MASK
+    )
+    
+    assert padded_logits.shape == logits.shape
+    assert attn_mask.shape[-1] == logits.shape[-1]
+
+  def test_attention_mask_broadcasting_correct_shapes(self):
+    """Test that broadcasting works when cache sizes match."""
+    batch_size, seq_len, num_heads, cache_size = 1, 100, 8, 512
+    
+    # Properly shaped mask and logits
+    attn_mask = jnp.ones((batch_size, seq_len, cache_size), dtype=bool)
+    logits = jax.random.normal(
+        jax.random.PRNGKey(0), (batch_size, seq_len, num_heads, cache_size)
+    )
+    
+    K_MASK = jnp.finfo(logits.dtype).min
+    
+    # This should work without errors
+    padded_logits = jnp.where(
+        (jnp.expand_dims(attn_mask, -2)), logits, K_MASK
+    )
+    
+    assert padded_logits.shape == logits.shape
+    # Verify that where mask is True, we get logits; where False, we get K_MASK
+    assert jnp.all(padded_logits[0, 0, 0, :] == logits[0, 0, 0, :])