How Hackers Are Making AI Weapons

Artificial Intelligence (AI) is reshaping the cybersecurity business. It is a double-edged sword that one can use as a security solution as well as a weapon by hackers. As AI becomes more widespread, there is a lot of misunderstanding about its capabilities and possible risks. We’ve watched movie scenes like machines taking over the planet and eliminating humanity. Such dystopian scenarios are famous in popular culture. On the other hand, many individuals understand the potential benefits of AI.

However, computer systems that can learn, reason, and act are still in the early stages of development. machine-learning needs massive volumes of data. This technology blends complicated algorithms, robotics, and physical sensors when applied to real-world systems like autonomous automobiles. While commercial deployment is simplified, giving AI access to data and letting it have any level of autonomy creates serious risks.

AI is changing the nature of cybersecurity

Cybersecurity solutions are frequently using AI. However, hackers are also using it to construct refined malware and launch assaults.

The cybersecurity sector is diversifying in an era of hyper-connectivity. And in this era data is one of the company’s most important assets. Therefore, industry specialists must be aware of some AI-driven cybersecurity trends.

Cybersecurity is predicted to be worth $248 billion by 2023. It owes to the increasing complexity of cyber threats that need powerful remedies.

Cybercrime is making a lot of money these days. Today, one can easily access plenty of resources. Therefore, even individuals with little technical knowledge can take part in it. Exploit kits ranging in price from a few hundred dollars to tens of thousands of dollars are available for sale. Moreover, a hacker might earn around $85,000 each month, according to Business Insider.

This is a lucrative and easily accessible sport that isn’t going away anytime soon. Furthermore, in the future, cyberattacks will only grow more difficult to detect. It will be more common and more sophisticated, putting all our connected devices at risk.

Of course, businesses can face significant losses. They can lose data, income, huge fines, and can incur the possibility of shutting down their operations.

As a result, the cybersecurity industry is likely to grow. Also, it will increase the number of vendors offering a wide range of solutions. Unfortunately, it’s an endless battle. Here we can’t overlook the fact that their solutions are only as effective as the next generation of malware.

How hackers can take advantage of AI?

Emerging technologies, such as AI, will continue to play a crucial role in this conflict. Hackers can exploit AI developments to launch cyberattacks. For example, DDoS attacks, MITM attacks, and DNS tunneling.

Consider CAPTCHA, a system that has been around for decades. One can use it to prevent credential stuffing by requiring non-human bots to read distorted text. A few years ago a Google study found that machine learning-based optical character recognition (OCR) technology could handle 99.8% of bots’ CAPTCHA challenges.

But, attackers are also using AI to crack passwords more quickly. Deep learning can aid in the speeding up of brute force attacks. For example, using millions of leaked passwords to train neural networks. Researchers were able to generate new passwords with a 26 percent success rate.

The black market for cybercrime tools and services offers AI the chance to improve efficiency and revenue.

The most significant concern about AI’s use in malware is that new strains can learn from detection occurrences. If a malware strain can figure out what compromised its identification, it can avoid repeating the same action or characteristic in the future.

For example, if a worm’s code was the source of its compromise, automated malware developers can rewrite it. Similarly, if specific qualities of behavior caused it to be visible, hackers might add randomization to evade pattern-matching rules.

✔️ Ransomware

The speed with which ransomware spreads in a network environment determines its effectiveness. Cybercriminals are already using AI to do this. For example, they use artificial intelligence to monitor firewall responses. Moreover, they try to find open ports that the security staff has overlooked.

There are several cases where firewall restrictions from different companies collide. AI is a fantastic tool for exploiting this flaw. Hackers have used AI in many recent breaches to get beyond firewall constraints.

Given their scope and intelligence, other attacks are AI-powered. Even on the black market, AI is embedded in exploit kits. For hackers, it’s a tremendously profitable tactic. Not to mention that the ransomware SDKs are filled with AI technology.

✔️ Automated Attacks

Hackers are using AI and machine learning to automate attacks on business networks. For example, cybercriminals can use them to create malware that detects vulnerabilities. This way, they can easily determine which payload to use to exploit them.

Malware does not have to communicate with command and control servers. Therefore, it can avoid detection. Attacks can be laser-focused with AI. Ans attackers can avoid the traditional slower, scattershot method that can tell a victim that they are under attack.

✔️ Fuzzing

Attackers also use AI to find new software flaws. There are already fuzzing tools available to help legitimate software developers. Also, they help penetration testers safeguard their programs and systems. However, as is often the case, the bad guys can exploit whatever tools the good people use.

In the global economy, AI and related systems are becoming widespread. Thus, the criminal underworld is following suit. Furthermore, the source code, data sets, and methodology required to develop and maintain these powerful capabilities are all public. Therefore, cyber thieves with a financial motive to exploit them will focus their efforts here.

Thus, data centers must use a zero-trust posture when it comes to detecting malicious automation.

✔️ Phishing

Employees have learned to spot phishing emails, especially those sent in bulk. However, AI allows attackers to customize each email for each receiver.

That’s where we’re seeing the first wave of machine learning algorithms being used as weapons. This includes reading an employee’s social media posts. Moreover, in the case of attackers who have already acquired access to a network or reading all the employee’s communications are examples of this.

Attackers can also use AI to infiltrate themselves into ongoing email conversations. An email that is part of a present conversation appears legitimate right away. Email thread hijacking is a highly effective method of infiltrating a system. Or it results in transmitting malware from one device to another.