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Threat researchers have uncovered a new sophisticated cyberattack method named “Conversation Overflow”, which manipulates cloaked emails to outsmart machine learning (ML) cyber defenses, leading to unauthorized access to their networks.
This deceptive tactic is part of a broader rising trend where cybercriminals are increasingly testing the waters, seeking vulnerabilities within emerging tech, especially targeting Large Language Models (LLM’s).
A Twist on the Defensive Tactic of Email Cloaking
Cloaking, when used by attackers, represents a twist on the defensive tactic of email cloaking. And instead of protecting email addresses, hackers disguise harmful content to bypass security measures and deceive recipients. To mention that this method primarily(but not exclusively) is used in phishing attacks.
Effective defenses against such tactics includes advanced threat detection, but also user education is very important. Last, but not least, implementing security protocols like multi-factor authentication greatly reduces the impact of phishing.
The Mechanism of the Conversation Overflow Method In LLM’s
In a typical ‘ Conversation Overflow’ attack, the email crafted by the hackers is divided into two segments. The first segment, while clearly visible to the recipient, is a often a decoy. This segment usually contains a call to action, for example a request to review a document or to check an unrecognized login attempt.
The catch, however, lies in the placement of this second segment, strategically located after long stretches of whitespace or hidden within the email’s code. The attackers bet that recipients, and more importantly, LLMs filters, will not detect or examine thoroughly this hidden content, and so classifying the email as non-threatening. From here on, sky is the limit once the malicious email pass through to the inbox and users interact with it.
AI: New Frontier & New Security Risk
However, there’s nothing new under the horizon, and the risk of vulnerability breaches was discussed ever since the inception of this new tech. And since the start of the blog, we are constantly monitoring for new threats, and one of those was already highlighted a few days ago: supply chain attacks via Large Language Models.
We still need to mention that LLM’s have also revolutionized cybersecurity, offering robust defenses against a myriad of digital threats. One of a recent example of fighting cybersecurity with AI, is enhancing thread hunting, and overall increase use of LLM’s to bolster cyber defenses.
These systems analyse patterns in data to identify anomalies that may signal a security breach.
However, the rise of sophisticated threats like “Conversation Overflow” poses new challenges. These attacks blur the line between ‘safe’ and malicious content, evading the binary classifications that LLM’s defenses are still rely upon.
Defending Against this New Attack
Defending against “Conversation Overflow” attacks requires planning on multiple fronts. We will mention two:
Implementing multi-layered email filtering systems that go beyond typical spam filters. Using a multitude combination of reputation-based, content-based, and anomaly-based filters. Reputation filters can block known malicious sources, content filters can scan for known phishing patterns, and anomaly filters can detect unusual email structures or hidden content. And an anomaly filter should be implemented whenever possible.
Second defensive improvement can be anomaly detection algorithms. Because of how the “Conversation Overflow” attack is usually operating, the primary focus should be on developing or implementing anomaly detection systems, that are not only focusing on the content but also patterns and structures within emails. For example unusually long messages, ones that contain a large amount of white space, should raise flags.
Machine learning models can and should be trained to detect these outliers. But as discussed here with the future release of GPT-5, it appears that the demand of high pace development in LLM’s domain often overlooks or downplay security measures. However this usually applies in the IT sector as a whole.
Developing defenses against “Conversation Overflow” attacks demands a proactive stance, combining technological solutions with ongoing people education.
Stay safe!
Bilbiography
Hadnagy, C. (2023). Phishing Dark Waters: The Offensive and Defensive Sides of Malicious Emails. Wiley.
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