Since its launch, to say that ChatGPT has created a buzz online would be somewhat of an understatement, and showcasing the capabilities of large language models and AI in cybersecurity has been one area where this innovation has sparked both interest and concern in equal measure.
Discussions around AI’s impact on the cybersecurity landscape have yet to cease, and rightly so. AI not only helps enterprise security operators enhance cybersecurity solutions, speed up threat analysis, and accelerate vulnerability remediation but also provides hackers with the means to launch more complex cyberattacks. This multifaceted impact makes the discussions and ramifications extremely complex. To make matters worse, traditional security measures are often inadequate in protecting AI models directly, and the security of AI itself remains a black box to the public.
To cover this topic in detail this article will delve deeply into three key areas:
- How large language models, including ChatGPT empower cyber attacks,
- How AI enhance cyber security defenses
- The security of large language models themselves
Empowering Cyber Attacks
Let’s begin by looking at the role that large language models, ChatGPT being one of them, can play in enabling the potency and frequency of cybersecurity attacks.
Large Language Models (LLMs) used for cyber attacks primarily focus on:
- Acquiring methods to use cybersecurity tools, identifying ways to exploit vulnerabilities, and the writing of malicious code, all of which serve as a knowledge base for attackers.
- Utilizing the programming capability of LLMs to obscure malicious code with the goal of evasion.
- Mass automation of phishing emails for social engineering attacks or generating social engineering dictionaries based on user information.
- Conducting code audits, vulnerability mining, testing, and exploitation of open-source or leaked source code.
- Combining single-point attack tools to form more powerful and complex multi-point attacks
It is clear that the automated generation abilities of large language models significantly impact the efficiency of security breaches by lowering the technical threshold and implementation costs of such intrusions and increasing the number of potential threats.
This has led to the consensus that LLMs pose a greater threat to cybersecurity than they help, as LLMs can easily transform an attacker’s ideas into code rapidly. Previously, a zero-day exploit with evasion capabilities could take a team of 3-10 hackers days or even weeks to develop, but leveraging the auto-generation capability of LLMs significantly shortens this process.. This leads to the issue that the cycle for weaponizing newly discovered vulnerabilities will be shortened, allowing cyber attack capabilities to evolve in sync.
Furthermore, utilizing ChatGPT’s automated auditing and vulnerability mining capabilities for open-source code allows attackers to master multiple zero-day vulnerabilities quickly at a lower cost. Some highly specialized open-source systems are not widely used by enterprises; hence, exploiting vulnerabilities in these systems is not cost-effective for attackers. However, ChatGPT changes this, shifting attackers’ zero-day exploration focus from widely used open-source software to all open-source software. As a result, it’s not unthinkable that certain specialized sectors that rarely experience security breaches could be caught off guard.
Lastly, large language models allow for the hurdle of language barriers to be navigated with far more ease, meaning social engineering and phishing might be the primary uses of such tools. A successful phishing attack relies on highly realistic content. Through AI-generated content (AIGC), phishing emails with various localized expressions can be quickly generated at scale. Utilizing ChatGPT’s role-playing ability, it can easily compose emails from different personas, making the content and tone more authentic, thereby significantly increasing the difficulty of discernment and increasing the success of the phishing campaign.
In summary, generative AI technology will lower the entry barriers to cybercrime and intensify enterprises’ existing risk profile, but there is no need for excessive worry. ChatGPT poses no new security threats to businesses, and professional security solutions are capable of responding to its current threats.
Enhancing Security Defenses
Obviously, the potential uses of large language models depend on the user; if they can empower cyber attacks, they can also empower cybersecurity defenses.
AI and large language models can empower enterprise-level security operations in the following ways:
- Acquire knowledge related to online security operations and improve the automation of responses to these security incidents.
- Conduct automated scans to detect any vulnerabilities at the code-level and provide reporting detailing the issues found along with recommendations for mitigation.
- Code generation to assist in the process of security operations management, this includes script generation and security policy command generation.
However, the effectiveness of the entire security system is hindered by its weakest link, leaving it vulnerable to exploitation by attackers who only need to identify a single point of vulnerability to succeed. Moreover, despite advancements in Extended Detection and Response (XDR), Security Information and Event Management (SIEM), Security Operations Center (SOC), and situational awareness, the correlation analysis of vast amounts of security data remains a formidable challenge. Context-based analysis and multi-modal parameter transfer learning are effective methods to address this issue. Since the release of ChatGPT, many security researchers and companies have made attempts in this field, providing a clear framework in the parsing of logs and data stream formats. However, in attempts at correlation analysis and emergency response, the process remains rather cumbersome, and the reliability of the response still needs further verification. Therefore, the impact of large language models on enterprise security operations, particularly in automated generation capabilities, pales in comparison to their potential impact on facilitating cyber attacks.
The characteristics of generative AI currently mean that it is unsuitable for situations requiring specialized cybersecurity analysis and emergency response. The ideal approach entails harnessing the power of the latest GPT-4 model, leveraging computing platforms for fine-tuning, additional training, and crafting bespoke models tailored for cybersecurity applications. This approach would facilitate the expansion of the cybersecurity knowledge base while bolstering analysis, decision-making, and code AIGC.
Security of Large Language Models Themselves
The threats facing AI models differ entirely from traditional cyber threats, and conventional security measures are difficult to apply directly to safeguarding AI models. AI models are most at threat from the following:
Privacy Leaks & Data Reconstruction
At present, ChatGPT does not provide any means of “privacy mode” or “incognito mode” to its users. This means that all the conversations and personal details shared by users can be collected as training data. Furthermore, OpenAI has yet to disclose its technical processes for data handling.
The ability to retain training data creates a potential risk of privacy breaches. For instance, when a generative model is trained on a specific dataset, it might supplement the original corpus during the questioning process. This can enable the model to reconstruct real data from the training set, thereby jeopardizing the privacy of the data.
Moreover, in several countries, there are insufficient systems in place to monitor and regulate the use of user data, leading to bans on ChatGPT usage by certain nations due to security concerns. A combination of policy and technological measures is essential to address this issue effectively.
From a technical standpoint, private deployment is the most effective solution, ensuring enterprise applications maintain security and control. However, to deploy private models, enterprises need the necessary talent and computing power to fine-tune them, which can be a costly operation. Currently, most enterprises lack the required talent and computing power to fine-tune their models, preventing them from opting for private deployment.
Model Theft
Attackers can steal a machine learning model’s structure, parameters, and hyperparameters by exploiting vulnerabilities in the request interfaces. This enables them to execute white-box attacks on the target model. For instance, attackers may design a series of questions related to a specific domain as inputs to ChatGPT, then utilize knowledge transfer techniques to train a smaller model that mimics ChatGPT’s capabilities in that domain. Through this process, the attackers can steal specific functionalities of ChatGPT.
Data Poisoning
If a model relies on user feedback for optimization, attackers can continually provide negative feedback to influence the quality of text generation in future model versions.
Semantic Injection
This risk was among the initial challenges ChatGPT encountered. Attackers can exploit nuanced language or manipulate the model into role-playing scenarios, bypassing existing security measures and restrictions to elicit accurate responses.
Summary
ChatGPT’s impact on cybersecurity has both positive and negative repercussions. In the short term, ChatGPT can make it easier for attackers to conduct cyber attacks and increase their efficiency. Conversely, it also aids defenders in responding to attacks more effectively. Despite this, a fundamental change in the nature of offense and defense in cybersecurity has yet to be brought about. ChatGPT is a human-computer interaction scenario, and if it is to be applied to deeper areas of security in the long term, it requires integration with security-specific data, intelligence, and deep learning models. This integration would develop a security-oriented GPT tailored for security scenarios, potentially instigating a qualitative shift in security paradigms.
