diff --git a/src/peft/tuners/lora/model.py b/src/peft/tuners/lora/model.py
index edb1273381..04294735d3 100644
--- a/src/peft/tuners/lora/model.py
+++ b/src/peft/tuners/lora/model.py
@@ -444,6 +444,19 @@ def _enable_peft_forward_hooks(self, *args, **kwargs):
         if self.training:
             raise ValueError("Cannot pass `adapter_names` when the model is in training mode.")
 
+        # Check that users only passed actually existing adapters.
+        # Note: We cannot do this on the layer level, as each individual layer may not have each adapter. Still, we want
+        # to check that there is at least one layer with the given name, or else something like typos can easily slip.
+        expected_adapters = set()
+        for layer in self.modules():
+            if isinstance(layer, LoraLayer):
+                expected_adapters |= layer.lora_A.keys()
+                expected_adapters |= layer.lora_embedding_A.keys()
+        unique_adapters = {name for name in adapter_names if name != "__base__"}
+        unexpected_adapters = unique_adapters - expected_adapters
+        if unexpected_adapters:
+            raise ValueError(f"Trying to infer with non-existing adapter(s): {', '.join(sorted(unexpected_adapters))}")
+
         hook_handles = []
         for module in self.modules():
             if isinstance(module, LoraLayer) or isinstance(module, ModulesToSaveWrapper):
diff --git a/tests/test_custom_models.py b/tests/test_custom_models.py
index aa747ad245..611b07bf97 100644
--- a/tests/test_custom_models.py
+++ b/tests/test_custom_models.py
@@ -3536,13 +3536,40 @@ def test_mixed_adapter_batches_lora_merged_raises(self, mlp_lora):
         # When there are merged adapters, passing adapter names should raise an error
         inputs = {
             "X": torch.arange(90).view(-1, 10).to(self.torch_device),
-            "adapter_names": ["default"] * 9,
+            "adapter_names": ["adapter0"] * 9,
         }
         mlp_lora.merge_adapter(["adapter0"])
         msg = r"Cannot pass `adapter_names` when there are merged adapters, please call `unmerge_adapter` first."
         with pytest.raises(ValueError, match=msg):
             mlp_lora.forward(**inputs)
 
+    def test_mixed_adapter_batches_lora_wrong_adapter_name_raises(self):
+        # Ensure that all of the adapter names that are being passed actually exist
+        torch.manual_seed(0)
+        x = torch.arange(90).view(-1, 10).to(self.torch_device)
+
+        base_model = MLP().to(self.torch_device).eval()
+        config = LoraConfig(target_modules=["lin0"], init_lora_weights=False)
+        peft_model = get_peft_model(base_model, config).eval()
+        peft_model.add_adapter(adapter_name="other", peft_config=config)
+
+        # sanity check: this works
+        peft_model.forward(x, adapter_names=["default"] * 5 + ["other"] * 4)
+
+        # check one correct and one incorrect adapter
+        msg = re.escape("Trying to infer with non-existing adapter(s): does-not-exist")
+        with pytest.raises(ValueError, match=msg):
+            peft_model.forward(x, adapter_names=["default"] * 5 + ["does-not-exist"] * 4)
+
+        # check two correct adapters and one incorrect adapter
+        with pytest.raises(ValueError, match=msg):
+            peft_model.forward(x, adapter_names=["default"] * 3 + ["does-not-exist"] * 4 + ["other"] * 2)
+
+        # check only incorrect adapters
+        msg = re.escape("Trying to infer with non-existing adapter(s): does-not-exist, other-does-not-exist")
+        with pytest.raises(ValueError, match=msg):
+            peft_model.forward(x, adapter_names=["does-not-exist"] * 5 + ["other-does-not-exist"] * 4)
+
     def test_mixed_adapter_batches_lora_with_dora_raises(self):
         # When there are DoRA adapters, passing adapter names should raise an error
         torch.manual_seed(0)