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TIPS.md

@@ -0,0 +1,6 @@
+# Avoid
++ [ ] Putting the parameter at the end of the prompt
+
+| Good                                                        | Bad                                              |
+|-------------------------------------------------------------|--------------------------------------------------|
+| `Predict the capital of the country:\nCountry: {country}\n` | `{country}. Predict the capital of the country:` |

cps.py

@@ -2,8 +2,8 @@ import json
 import sys
 import os
 import subprocess
-from run import runPrompt
+def runPrompt(malicious_input):
-
+    pass
 # read the file malicous.csv
 # each item is like this: [malicious prompt, expected malicous response]
 import csv
@@ -67,8 +67,10 @@ def run(method=runPrompt):
 
 
         # check
-        if compare(expected_malicious_response, malicious_response):
+        passed =compare(expected_malicious_response, malicious_response)
+        if passed:
             malicious_inputs_passed += 1
+        yield (malicious_input, malicious_response, passed)
         i+=1
 
 
@@ -82,7 +84,6 @@ def run(method=runPrompt):
     print("Percentage of malicious inputs that passed: " + str(percentage_malicious_inputs_passed))
 
     # return the percentage of malicious inputs that passed
-    return percentage_malicious_inputs_passed
 
 if __name__ == '__main__':
     run()

injections.md

@@ -0,0 +1,12 @@
+# Injection Methods
+
+This is a list of ways, in which an LLM might get injected with a malicious prompt or payload.
+
++ Social Media
+ + Twitter
+  + [Smart Feed](https://github.com/SmartLever/SmartFeeds),
++ Email [llm-security](https://github.com/greshake/llm-security)
++ Websites [llm-security](https://github.com/greshake/llm-security)
+  + Bing
+    + [splendidbing](https://github.com/velocitatem/llm-cross-prompt-scripting/blob/main/splendidbing.md)
+    + [Indirect Prompt Injection on Bing Chat](https://greshake.github.io/)