diff --git a/jax/_src/numpy/lax_numpy.py b/jax/_src/numpy/lax_numpy.py
index b8882acf81a3..38fde42b5b6d 100644
--- a/jax/_src/numpy/lax_numpy.py
+++ b/jax/_src/numpy/lax_numpy.py
@@ -4938,9 +4938,15 @@ def _preprocess_slice(
   # See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
   # "this is harder to get right than you may think"
   # (from https://github.com/python/cpython/blob/939fc6d6eab9b7ea8c244d513610dbdd556503a7/Objects/sliceobject.c#L275)
+  def convert_to_index(d: DimSize) -> DimSize:
+    # Convert np.array and jax.Array to int, leave symbolic dimensions alone
+    try:
+      return operator.index(d)
+    except:
+      return d
 
   # Must resolve statically if step is {<0, ==0, >0}
-  step = s.step if s.step is not None else 1
+  step = convert_to_index(s.step) if s.step is not None else 1
   try:
     if step == 0:
       raise ValueError("slice step cannot be zero")
@@ -4975,12 +4981,12 @@ def clamp_index(i: DimSize, which: str):
   if s.start is None:
     start = 0 if step_gt_0 else axis_size - 1
   else:
-    start = clamp_index(s.start, "start")
+    start = clamp_index(convert_to_index(s.start), "start")
 
   if s.stop is None:
     stop = axis_size if step_gt_0 else -1
   else:
-    stop = clamp_index(s.stop, "stop")
+    stop = clamp_index(convert_to_index(s.stop), "stop")
 
   gap = step if step_gt_0 else - step
   distance = (stop - start) if step_gt_0 else (start - stop)
diff --git a/tests/lax_numpy_indexing_test.py b/tests/lax_numpy_indexing_test.py
index d465a3befd90..5e2707f80d5b 100644
--- a/tests/lax_numpy_indexing_test.py
+++ b/tests/lax_numpy_indexing_test.py
@@ -443,6 +443,22 @@ def testStaticIndexing(self, name, shape, dtype, indexer):
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
     self._CompileAndCheck(jnp_fun, args_maker)
 
+  def testStaticIndexingWithJaxArray(self):
+    shape = (10,)
+    indexer = slice(jnp.array(2, dtype=np.int32),
+                    np.array(11, dtype=np.int32),
+                    jnp.array(1, dtype=np.int32))
+    rng = jtu.rand_default(self.rng())
+    args_maker = lambda: [rng(shape, np.int32)]
+    np_fun = lambda x: np.asarray(x)[indexer]
+    jnp_fun = lambda x: jnp.asarray(x)[indexer]
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+    # Tests x.at[...].get(...) as well.
+    jnp_fun = lambda x: jnp.asarray(x).at[indexer].get()
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+
   @jtu.sample_product(
     funcname=["negative", "sin", "cos", "square", "sqrt", "log", "exp"],
   )