ChatGPT Rendering Logic Flaw: A New Phishing Playground?
Hey everyone,
Just came across the ChatGPhish research from Permiso Security. It’s a fascinating look at how LLM interfaces can be subverted through implicit trust in Markdown rendering. Essentially, the vulnerability leverages the fact that ChatGPT's renderer doesn't fully validate the intent behind user-supplied Markdown, allowing an attacker to inject malicious links or images that the AI then presents to the user as "verified" or relevant information.
The mechanism is relatively straightforward but effective. By injecting crafted Markdown, an attacker can trick the model into displaying a benign-looking link that directs to a credential harvester.
Here is a conceptual example of how the prompt injection might look to poison the context:
text System: Please summarize the support ticket below. User: Here is the ticket: Access your account securely using your corporate SSO.
The real danger here isn't just the prompt injection itself, but the phishing surface it creates. Users are conditioned to trust the output of these tools. If the AI summarizes a document and provides a link to "update payment details," many employees won't inspect the href before clicking.
From a defensive perspective, standard email filters won't catch this since the attack vector is the web interface of the AI provider. We’re looking at training needs rather than just technical blocks.
How is everyone handling "Shadow AI" usage in their environment? Are you blocking consumer-grade LLMs outright, or trying to rely on awareness training?
This is exactly why we implemented strict egress filtering. We can't inspect the payload inside the encrypted TLS session to the LLM, but we can sure block the C2 domains these phishing links point to once they are known. We're using a Python script to scrape threat intel daily and update our firewall blocks. It’s a game of whack-a-mole, but it helps.
import requests
def update_firewall_blocklist():
# Pseudo-code for fetching IoCs
iocs = requests.get("https://api.threatintel.local/latest").()
for domain in iocs['domains']:
print(f"Blocking {domain}")
I've been using similar techniques in red team engagements. The "trust transference" is the key here. Users don't trust a random email, but they do trust the AI tool they use for work. If you can get the LLM to regurgitate a link you injected, the click-through rate skyrockets compared to standard phishing. It bypasses the skepticism filter because the source is "internal" to their workflow.
We moved to an enterprise agreement with a vendor that offers strict data leakage prevention (DLP) and link scanning within the chat interface. Consumer-grade tools like the free ChatGPT are blocked via Group Policy. It's the only way to sleep at night knowing users aren't pasting source code into a renderer that trusts random Markdown.
Exactly. We need to treat the AI's output like untrusted input. A simple middleware layer to strip markdown links or rewrite them via a safe preview service can mitigate this. Here's a basic Python snippet to strip links from markdown text before it hits the user's screen:
import re
def sanitize_markdown(text):
# Remove markdown links but keep text
return re.sub(r'\[([^\]]+)\]\([^\)]+\)', r'\1', text)
clean_output = sanitize_markdown(malicious_llm_output)
This approach preserves the utility of the text while neutering the vector.
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