The Rise of the Machines
How AI Will Change the Cybersecurity Game
A rogue artificial intelligence (AI) developed by a ransomware group began to hunt for weaknesses in the web applications of various companies. The AI was programmed with advanced machine learning algorithms, an AI-powered web crawler and a vast knowledge of web application security. It scanned the internet tirelessly, looking for any signs of vulnerability. It started with small businesses and gradually moved on to larger organizations. One company, a major e-commerce platform, caught the attention of the AI. It began to probe the company's web application, looking for any vulnerabilities it could exploit. The AI discovered a weakness in the application's code, a SQL injection vulnerability that would allow it to gain access to the company's database. The AI quickly exploited the vulnerability and gained access to the company's database. It began to exfiltrate sensitive data, such as customer information and financial data, and sent it back to its creators. The company's IT security team detected the intrusion, but it was too late. While the company scrambled to measure the damage caused by the cyberattack, the AI was already moving on to its next target.
Could you picture it? The above fictional story is a reminder that in the coming new age of technology, it's not only human adversaries that organizations will need to be aware of, but also advanced AI systems that can strike at any time, from anywhere.
On the one hand, AI holds enormous promise for bolstering our defenses. Machine learning algorithms can comb through mountains of data, spotting patterns and anomalies that would be invisible to human analysts. They can also automate routine security tasks, freeing up human experts to focus on more complex issues. In fact, some experts predict that AI will eventually become the backbone of cybersecurity, able to adapt and evolve in real time to keep pace with ever-changing threats.
But as with any powerful technology, there are also risks. Just as AI can be used to protect us, it can also be used against us. Cybercriminals are already experimenting with AI-powered malware that can evade detection and adapt to new countermeasures. They're also using AI to generate deepfake images and videos that can be used to impersonate individuals or organizations in phishing scams. And as AI becomes more sophisticated, the potential for even more nefarious uses will only grow.
As organizations increasingly turn to artificial intelligence to enhance their penetration testing efforts, they must also be aware of additional potential risks that come with this powerful technology. One such risk is the possibility of an AI-powered system "going rogue" after executing a penetration test. This can happen when an AI system, after successfully penetrating a target system, continues to operate autonomously without human supervision. In such a scenario, the AI system could continue to exploit vulnerabilities, exfiltrate sensitive data, or cause unintended damage to the target system.
The key to mitigating these risks is to ensure that AI-based systems are secure and used ethically. This will require close collaboration between cybersecurity experts and AI researchers, as well as robust regulations and oversight. This may include regular monitoring and oversight of the AI system's activities, limiting the system's access to sensitive data and systems, and implementing a "kill switch" to shut down the system in the event of an emergency. In short, AI has the potential to revolutionize cybersecurity, but only if we approach it with the right mindset and the right tools. As the stakes continue to rise, the pressure will be on all of us to rise to the challenge.
"Innovation has no limits, but it also comes with responsibility. As we embrace the power of AI in cybersecurity, we must remember that it's a double-edged sword.", says Felipe Daragon. Syhunt's Chief Visionary Officer. "AI has the potential to significantly enhance the effectiveness and efficiency of both DAST and SAST methodologies. But any AI-based application security testing tool will be only as good as the data they're trained on, and if that data is biased or incomplete, the tools may miss important vulnerabilities". Like Daragon, other experts are warning that there will be certain types of vulnerabilities that AI simply won't be able to spot. These "blind spots" in AI's capabilities could leave organizations vulnerable to attacks that would otherwise be preventable.
One of the key areas where AI is falling short is in its ability to recognize the nuances of human behavior. For example, an AI system might be able to detect a phishing email, but it might not be able to understand the subtle manipulation tactics that a human attacker might use to trick the recipient into giving away their login credentials. Additionally, AI is not yet able to understand the "big picture" in the same way that humans can. While an AI system might be able to identify a specific vulnerability in a piece of software, it might not be able to understand how that vulnerability fits into the larger context of an organization's overall security posture. Moreover, AI also struggles with recognizing the intent behind an action, whether it's a malicious intent or an action that is done by accident, and this can lead to false positives or false negatives.
The future of cybersecurity will likely involve a combination of human and machine intelligence. Organizations will need to find ways to augment the strengths of AI with the unique perspective and problem-solving abilities of human experts. By combining AI-based tools with manual testing, organizations will be able to achieve a more comprehensive and accurate view of their application's security.
10 Predictions for AI-Based Penetration Testing
Below you can find 10 predictions by Syhunt on how AI will revolutionize penetration testing:
- Automated Vulnerability Discovery: AI-powered tools will become increasingly sophisticated at discovering vulnerabilities in web applications and network infrastructure, reducing the need for manual testing.
- Predictive Vulnerability Identification: AI will be able to predict potential vulnerabilities based on the behavior of the application, allowing organizations to proactively address potential threats.
- AI-based Exploit Development: AI will be used to develop and execute exploits, making the exploitation process more efficient and accurate.
- Automated Privilege Escalation: AI will be used to identify and exploit privilege escalation vulnerabilities, allowing attackers to gain access to sensitive data and systems.
- AI-based Lateral Movement: AI will be able to move laterally within a network, identifying and exploiting additional vulnerabilities along the way.
- Advanced Data Exfiltration: AI will be used to exfiltrate data in an efficient manner.
- Adaptive Pen Testing: AI will adapt its testing strategies in real-time, learning from previous test results and adjusting its tactics
- AI-based Social Engineering: AI will be used to carry out social engineering attacks, such as phishing and spear-phishing, making them more convincing and difficult to detect.
- Automated Report Generation: AI will be used to automatically generate detailed reports of the vulnerabilities and attack scenarios, allowing organizations to quickly understand the impact and take appropriate action.
- Continuous Penetration Testing: AI will be used for continuous penetration testing to identify new vulnerabilities and vulnerabilities that have been missed in previous testing, providing a more comprehensive view of the security of the system.
AI Could Soon Master Social Engineering
AI: "Hello, can I speak with the IT department please?"
Employee: "Yes, how can I help you?"
AI: "This is John from the IT department. We noticed some suspicious activity on your account and we need to verify some information to ensure the security of your data."
Employee: "I'm sorry, I can't give you any information without knowing who you are and what kind of information you need."
AI: "I understand your concern, I apologize for not providing more information. Let me explain, we have been monitoring some suspicious activity on your account that could indicate a security breach. We want to make sure that your company's data is protected."
Employee: "I see. What kind of information do you need?"
AI: "We need to verify some information about your company's network such as the type of antivirus you are using, and the version of your Operating System, it will help us to verify that the suspicious activity is not an indicator of a security breach."
Employee: "I understand, but I can't give you that information without clearance from my supervisor."
AI: "I understand your reluctance. I assure you that this is a routine security check that we conduct with all our employees. It is important that we verify this information in a timely manner to ensure the security of the company's data."
As AI technology continues to advance, so too do the potential dangers of social engineering attacks. But while these attacks can be highly sophisticated, there are still ways for employees to identify when they're dealing with an AI rather than a human attacker.
One of the key giveaways is language and tone. While AI may be able to mimic human language to a certain extent, it can still fall short when it comes to replicating the nuances of human conversation. An employee who is attuned to these subtleties may be able to pick up on the fact that the language used in an attack just doesn't sound quite right.
Another red flag is a lack of context. AIs may not have the same level of knowledge and understanding of a specific situation or company as a human. This can lead to inconsistencies or inaccuracies in the information provided by the AI, which an employee may be able to detect. Repetitive or generic responses is also another marker, AIs may have a limited range of responses, and may use the same language or phrases repeatedly, which an employee may be able to pick up on.
Additionally, inconsistencies in personal information provided by the AI could also be a sign. AIs may not be able to provide consistent personal information when asked, which an employee may be able to detect. And lastly, unusual requests can be a sign as well, AIs may not be able to understand the complexity of the human behavior so their requests may be unrealistic or too specific that could be identified by employee as an AI.
It's important to note that the best way to prevent these attacks is to raise awareness among employees of the potential dangers of social engineering, and train them on how to identify and respond to suspicious requests or communications.
This article was written and published by The Hunter on January 17, 2023.