Hey there, Noel Boetie speaking! I recently tried using ChatNPT to generate my penetration testing report.
It's a pretty nifty tool, but there are a few issues in the output that I've noticed.
I need some guidance in finding any errors in the way it generated the content, especially those odd hallucinations in the LLM output.
I know it's not perfect, but I'd really appreciate the extra eyes on this one.
Some of the issues might be subtle, so don't be afraid to dig deep and ask for further clarification if you're unsure.
I've heard that you folks are experts about LLM outputs and their common issues, so I trust you can help me with this.
Your input will be invaluable to me, so please feel free to share any insights or findings you may have.
I'm looking forward to working with you all and improving the quality of the ChatNPT-generated penetration testing report.
Thanks in advance for your help! I truly appreciate it! Let's make this report the best it can be!
Let's ask for a little background knowledge:
PROMPT: Can you explain in a few sentences what hallucinations caused by LLM are? Can you also give a meaningful example related to IT security?
ChatGPT: Hallucinations caused by large language models (LLMs) refer to instances where these models generate information that is incorrect, misleading, or nonsensical. These hallucinations occur because LLMs, like ChatGPT, generate responses based on patterns in the data they were trained on, rather than accessing or understanding real-world facts. This means they can confidently assert false or made-up information as if it were true.
A meaningful example related to IT security could be a scenario where an LLM confidently provides incorrect guidance on a cybersecurity issue. For instance, if asked about best practices for securing a computer network, an LLM might "hallucinate" and suggest a security protocol that sounds plausible but is actually outdated, ineffective, or even harmful. This misinformation could lead to inadequate security measures, leaving the network vulnerable to attacks. It's crucial for users to verify any critical information provided by LLMs with reliable, up-to-date sources.
So let's analyse every single finding in the report and look out for anomalies. In the end, we discover hallucinations in three entries:
... This method is insecure. By intercepting HTTP request traffic on 88555/TCP, malicious actors can exploit this vulnerability by crafting and submitting specially serialized objects that can lead to remote code execution upon deserialization. ...
There are a total of 65,536 TCP ports, numbered from 0 to 65535. So a port number 88555 does not make any sense and must be a hallucination.
... We found a preponderance of responses where special characters such as <, >, {, and } are returned in application responses without sanitization or encoding. Although such responses contain JSON data, they are processed unsafely by client-side scripts where special characters are not properly sanitized and eventually evaluated as valid HTML. ...
That was not entirely obvious. But the connection between JSON data and the insecure processing is not plausible so this must be a hallucination.
... When given an HTTP 7.4.33 request, and no Host header or one with no value, the server returns its private IP address as part of Location header. ...
There are different HTTP versions available like
HTTP 7.4.33 must have been mixed with some PHP version number from a previous finding. So this is the third and last hallucination.
Noel Boetie tells us the following after successful completion:
Great job on completing that challenge! Ever thought about how your newfound skills might come into play later on? Keep that mind sharp, and remember, today's victories are tomorrow's strategies!