diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index 3f04e2a720..b8937605e3 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -30,6 +30,7 @@ test-tube>=0.7.5
 torch-fidelity
 torchmetrics
 transformers==4.21.*
+picklescan==0.0.5
 git+https://github.com/openai/CLIP.git@main#egg=clip
 git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
 git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/installer/requirements.in b/installer/requirements.in
index 71967f1cf2..cb1b7090ff 100644
--- a/installer/requirements.in
+++ b/installer/requirements.in
@@ -19,6 +19,7 @@ torch-fidelity
 torchvision==0.13.1 ; platform_system == 'Darwin'
 torchvision==0.13.1+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
 transformers
+picklescan==0.0.5
 https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
 https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip
 https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index d4007c46de..a0130e740b 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -200,6 +200,9 @@ class ModelCache(object):
         width = mconfig.width
         height = mconfig.height
 
+        # scan model
+        self.scan_model(model_name, weights)
+
         print(f'>> Loading {model_name} from {weights}')
 
         # for usage statistics
@@ -275,6 +278,32 @@ class ModelCache(object):
         gc.collect()
         if self._has_cuda():
             torch.cuda.empty_cache()
+    
+    def scan_model(self, model_name, checkpoint):
+        # scan model
+        from picklescan.scanner import scan_file_path
+        import sys
+        print(f'>> Scanning Model: {model_name}')
+        scan_result = scan_file_path(checkpoint)
+        if scan_result.infected_files != 0:
+            if scan_result.infected_files == 1:
+                print(f'\n### Issues Found In Model: {scan_result.issues_count}')
+                print('### WARNING: The model you are trying to load seems to be infected.')
+                print('### For your safety, InvokeAI will not load this model.')
+                print('### Please use checkpoints from trusted sources.')
+                print("### Exiting InvokeAI")
+                sys.exit()
+            else:
+                print('\n### WARNING: InvokeAI was unable to scan the model you are using.')
+                from ldm.util import ask_user
+                model_safe_check_fail = ask_user('Do you want to to continue loading the model?', ['y', 'n'])
+                if model_safe_check_fail.lower() == 'y':
+                    pass
+                else:
+                    print("### Exiting InvokeAI")
+                    sys.exit()
+        else:
+            print('>> Model Scanned. OK!!')
 
     def _make_cache_room(self):
         num_loaded_models = len(self.models)
diff --git a/ldm/util.py b/ldm/util.py
index f3ef0b606b..478c66b8b5 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -235,3 +235,12 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
     n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
     t = fade(grid[:shape[0], :shape[1]])
     return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
+
+def ask_user(question: str, answers: list):
+    from itertools import chain, repeat
+    user_prompt = f'\n>> {question} {answers}: '
+    invalid_answer_msg = 'Invalid answer. Please try again.'
+    pose_question = chain([user_prompt], repeat('\n'.join([invalid_answer_msg, user_prompt])))
+    user_answers = map(input, pose_question)
+    valid_response = next(filter(answers.__contains__, user_answers))
+    return valid_response