In the ever-changing cybersecurity landscape, businesses and individuals find themselves engaged in a fierce battle against the rise of cybercrime, which continues to grow in complexity and frequency. Despite significant investments in cutting-edge cybersecurity solutions, the financial toll of cybercrime persists, with costs increase every year. Among the myriad of cyber threats, social engineering attacks, including Phishing And Business Email Compromise (BEC), stand out for their prevalence and the multifaceted impact they have on businesses. These attacks exploit human psychology rather than technical vulnerabilities, making them particularly insidious and difficult to counter.
A shift to innovative approaches
As organizations grapple with these challenges, attention is increasingly focused on innovative strategies to strengthen defenses against social engineering. Security Awareness Training has become a critical pillar of this effort, aimed at equipping individuals with the knowledge and tools necessary to recognize and respond to such threats. Therein lies the potential for artificial intelligence (AI) large language models (LLMs) to revolutionize the fight against social engineering.
For example, LLMs can generate communications that mimic phishing emails, but are designed to educate users about the characteristics of such attacks, thereby turning breach attempts into real-time learning opportunities. Additionally, LLMs can be trained to identify language patterns and strategies used by cybercriminals, thereby predicting and neutralizing attacks before they reach their targets. By analyzing the evolving tactics of social engineers, AI can help craft deceptive countermeasures that mislead attackers, waste their resources, and ultimately deter them from pursuing their malicious goals.
THE integration of LLM AI into cybersecurity strategies represents a paradigm shift from reactive to proactive defense mechanisms. By targeting the psychological underpinnings of social engineering, organizations can disrupt the effectiveness of these attacks, not just through technical barriers, but also by manipulating the very biases exploited by attackers.
However, while this approach is essential, it only addresses one side of the equation. The fascinating aspect of social engineering is that the attackers, despite their nefarious intentions, are human and, as such, are subject to inherent biases and psychological patterns. This awareness opens a new battlefield: the minds of the attackers themselves. AI LLMs, with their ability to process and generate human-like text, offer a unique avenue to psychologically “hack” social engineers.
The company strikes back
These considerations sparked the conceptualization of a “HackBot” to reverse social engineering tactics. That’s the subject of the latest research paper by Mary Aiken and Diane Janosek of Capitol Technology University and Michael Lundie, Adam Amos-Binks and Kira Lindke of Applied Research Associates.
Titled “The Business Strikes Back: Conceptualizing the HackBot – Reversing Social Engineering in the Context of Cyber Defense” (a clear nod to StarTrek’s “The Empire Strikes Back”), the document suggests “the conceptualization of “HackBot” – an automated response innovation, specifically designed to reverse social engineering attacks in cyber defense contexts.
The paper’s authors note that there is a paradigm shift from passive to active cyberdefense, the researchers assess “whether disruptive cognitive techniques targeting the mental limitations and biases of the attacker could be applied.” A recent National Cyber Force (NCF) report explained how the UK is taking a new approach to conducting offensive cyber operations with a focus on disrupting information environments.
This approach introduces the doctrine of the “cognitive effect”, which aims to counter accusatory behavior by exploiting the use of digital technology. Therefore, offensive cyber operations can restrict an adversary’s ability to collect, disseminate, and trust information.
The HackBot concept recognizes that cybersecurity involves both technological elements and human psychology and that understanding the human side of cyberattacks is crucial for effective defense. The authors highlight ten psychological vulnerabilities associated with cybercriminals, which the “HackBot” could exploit to establish respective counter-attack patterns. These vulnerabilities include:
- Trust bias
- Online disinhibition
- Impulsiveness
- Risk taking
- Cognitive overload
- Search for reward
- Paraphilias
- Dark Personality Traits
- Affective and emotional attributes
- Attentional tunneling
According to the research paper, the task of the “HackBot” is to generate text that can be used as part of a social engineering attack. This involves understanding the context of the specific type of attack, being able to handle a variety of different attacks, and producing dialogue typical of the attacker’s target. One way to approach this problem is to use pre-trained LLMs and refine them with real incident reports of social engineering attacks. LLMs are particularly well suited to this task because they are widely available, require relatively few downstream task examples, and can easily adapt to new contexts.
The goal of the “HackBot” is to serve “as an effective honeypot for cyber attackers, engaging them in prolonged and deceptive interactions, distracting and draining resources, and specifically conceptualized to thwart social engineering attacks in cyber defense contexts”.
In conclusion, as cyber threats become increasingly sophisticated, leveraging AI LLMs to thwart social engineering by “hacking” into attackers’ psychological vulnerabilities offers a promising frontier in cybersecurity. This approach not only strengthens existing defensive measures, but also paves the way for a more adaptive, intelligent and ultimately effective cybersecurity posture. In the arms race against cybercrime, the psychological hacking strategy represents a crucial step in turning the tide against social engineers.
Editor’s Note: The opinions expressed in this guest author article are solely those of the contributor and do not necessarily reflect those of Tripwire.
