diff --git a/jax/_src/numpy/ufunc_api.py b/jax/_src/numpy/ufunc_api.py
index 0ef7aeba8..42c14aef1 100644
--- a/jax/_src/numpy/ufunc_api.py
+++ b/jax/_src/numpy/ufunc_api.py
@@ -20,6 +20,7 @@ np.ufunc.at(). Instead, you can pass inplace=False and capture the result; e.g.
 >>> arr = jnp.add.at(arr, ind, val, inplace=False)
 """
 from functools import partial
+import math
 import operator
 from typing import Any, Callable, Optional
 
@@ -264,6 +265,7 @@ class ufunc:
       return self._at_via_scan(a, indices, b)
 
   def _at_via_scan(self, a: ArrayLike, indices: Any, *args: Any) -> Array:
+    assert len(args) in {0, 1}
     check_arraylike(f"{self.__name__}.at", a, *args)
     dtype = jax.eval_shape(self._func, lax_internal._one(a), *(lax_internal._one(arg) for arg in args)).dtype
     a = lax_internal.asarray(a).astype(dtype)
@@ -277,7 +279,9 @@ class ufunc:
     if not shape:
       return a.at[indices].set(self._call(a.at[indices].get(), *args))
 
-    args = tuple(_broadcast_to(arg, shape).ravel() for arg in args)
+    if args:
+      arg = _broadcast_to(args[0], (*shape, *args[0].shape[len(shape):]))
+      args = (arg.reshape(math.prod(shape), *args[0].shape[len(shape):]),)
     indices = [idx if isinstance(idx, slice) else _broadcast_to(idx, shape).ravel() for idx in indices]
 
     def scan_fun(carry, x):
diff --git a/tests/lax_numpy_ufuncs_test.py b/tests/lax_numpy_ufuncs_test.py
index cace4d8d4..8f90a997e 100644
--- a/tests/lax_numpy_ufuncs_test.py
+++ b/tests/lax_numpy_ufuncs_test.py
@@ -289,6 +289,19 @@ class LaxNumpyUfuncTests(jtu.JaxTestCase):
     self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
     self._CompileAndCheck(jnp_fun, args_maker)
 
+  def test_at_broadcasting(self):
+    # Regression test for https://github.com/google/jax/issues/18004
+    args_maker = lambda: [np.ones((5, 3)), np.array([0, 4, 2]),
+                          np.arange(9.0).reshape(3, 3)]
+    def np_fun(x, idx, y):
+      x_copy = np.copy(x)
+      np.add.at(x_copy, idx, y)
+      return x_copy
+    jnp_fun = partial(jnp.frompyfunc(jnp.add, nin=2, nout=1, identity=0).at, inplace=False)
+
+    self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+
   @jtu.sample_product(
       SCALAR_FUNCS,
       [{'shape': shape, 'axis': axis}