Generative AI, which includes technologies like Generative Adversarial Networks (GANs) and language models like GPT-3, has the potential to introduce several cybersecurity risks and challenges. These risks arise from the capabilities of generative AI in creating highly realistic and convincing content, as well as its ability to automate and optimize various malicious activities.

One notable risk is its potential to enhance password-cracking techniques, as AI algorithms can swiftly gain insights from password databases and generate probable password combinations — posing a substantial threat, particularly to passwords that are weak or commonly used.

Key Takeaways

Generative AI poses cybersecurity risks by creating convincing content and enhancing malicious activities.
AI-driven password guessers can analyze large datasets to identify common password patterns, making weak passwords especially vulnerable.
Password managers play a crucial role in generating and storing unique passwords, enhancing overall password security.
Two-factor authentication adds an additional layer of security, making unauthorized access significantly more difficult.
Ongoing vigilance and continuous updates to security measures are essential to stay ahead of evolving AI-driven threats.

AI-Powered Password-Cracking Tools

AI-powered password-cracking tools utilize artificial intelligence and machine learning algorithms to efficiently guess or crack passwords. These tools can learn from existing password data, recognize patterns, and automate various techniques to compromise user accounts, making them a significant cybersecurity threat. Some of the more commonly used tools include:

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PassGAN

PassGAN is a well-known AI-powered tool that uses a generative adversarial network (GAN) to generate and guess passwords. It can learn from existing password databases and generate likely password combinations.

HashCat

HashCat is a popular password-cracking tool that utilizes AI and rule-based systems to optimize and speed up the process of guessing passwords. Rules can be created based on patterns and common password structures.

RockYou2021

This is an example of a leaked password database that contains millions of real-world passwords. AI tools can analyze these datasets to learn common password patterns and increase their success rates in cracking passwords.

Deep Learning Password Cracking

AI researchers have experimented with deep learning techniques, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs), to improve password-cracking capabilities. These models can learn complex patterns and structures in passwords.

Pattern Recognition Tools

AI can be used to recognize patterns in passwords, such as common substitutions (e.g., “P@ssw0rd” for “Password”) or keyboard patterns (e.g., “123456” or “qwerty”).

Potential Dangers Posed by AI-driven Password Guessers

AI-driven password guessers significantly expand the threat landscape. They contribute to massive data breaches by compromising numerous accounts. When coupled with large datasets of breached credentials, these tools become even more potent, exposing sensitive user information and potentially leading to significant financial and reputational damage.

Users with weak or easily guessable passwords are particularly vulnerable. AI guessers can swiftly identify and exploit these vulnerabilities, putting individuals and organizations at risk.

Plus, AI-driven guessers can adapt to changing circumstances and improve their techniques over time. This necessitates ongoing vigilance and continuous improvement of security measures to stay ahead of evolving threats.

Common Methods Utilized by AI in Password Cracking

Modern cyber threat techniques are usually evolved forms or combinations of traditional methods. Here are some infamous techniques hackers have been exercising to exploit the seamless power of technology.

Pattern Recognition

AI algorithms can recognize patterns and trends in passwords, such as the use of common phrases, keyboard patterns (e.g., “123456” or “qwerty”), or predictable substitutions (e.g., “P@ssw0rd” for “Password”). AI-based systems can efficiently identify and exploit these patterns in password guessing.

Data Mining

AI can mine and analyze large datasets, including breached password databases, to identify common password choices and patterns. By learning from these datasets, AI can better predict and guess passwords used by individuals across different platforms and services.

Dictionary Attacks

Dictionary attacks use a predefined list of words, phrases, or commonly used passwords (a “dictionary”) to guess a target’s password. AI can enhance dictionary attacks by combining words, applying common substitutions (e.g., replacing ‘o’ with ‘0’), and manipulating the dictionary list to generate more variations.

Credential Stuffing

Credential stuffing is the automated process of using stolen username-password pairs from one site to gain unauthorized access to other accounts where users have reused the same credentials. Over 50% of users have the same password for multiple accounts — making the attacker’s job much easier.

AI-driven bots excel at automating credential-stuffing attacks and rapidly testing stolen credentials across various online services.

Brute Force Attacks

Brute force attacks involve systematically trying all possible combinations of characters until the correct password is found. AI can accelerate this process by predicting which combinations are more likely based on patterns and common password structures.

Keyboard Sound-Based Attacks

A recent study conducted by Cornell University in the United States has revealed that an AI model, when activated on a nearby smartphone, demonstrated the ability to replicate a typed password on a laptop with an impressive accuracy rate of 95 percent. This AI model, developed by a team of computer scientists based in the United Kingdom, was specifically trained to identify keystrokes, a capability that has raised concerns about potential misuse by hackers.

The study found that the AI tool exhibited remarkable precision in deciphering keystrokes, even when it utilized a laptop’s microphone during a Zoom video conference. The study emphasized that the widespread availability of keyboard acoustic signals not only makes them a readily accessible method for cyberattacks but also leads individuals to underestimate the potential risks associated with such attacks, thus discouraging them from taking precautions to conceal their input. To raise awareness about such emerging threats, cybersecurity experts often discuss these risks in an online webinar meeting, helping individuals and organizations take necessary precautions.

Protecting Your Passwords Against AI Password Guessers

Wise people never outgrow the belief that prevention is better than cure! So, here’s what you can do to impede hackers from cracking your passwords using AI:

Strong and Unique Passwords

Strong passwords are a critical defense against AI-driven password guessers. They make it significantly harder for these tools to crack your accounts. For creating unique passwords, try:

Using a combination of uppercase and lowercase letters, numbers, and special characters.
Avoiding easily guessable information like birthdays, names, or common phrases.
Using passphrase-based passwords, combining random words, or making up a memorable phrase.
Ensuring your passwords are long (at least 12-16 characters) and unrelated to personal information.
Using different passwords for each online account to prevent a breach in one account from compromising others.

Password Managers

Password managers are indispensable tools for safeguarding your passwords effectively. They can generate, store, and auto-fill complex, unique passwords for each of your accounts, eliminating the need to create and remember them manually. Many password managers sync your passwords across multiple devices, ensuring you have access to your passwords wherever you go. They often provide security breach alerts, notifying you if any of your accounts have been compromised.

Two-Factor Authentication (2FA)

Two-factor authentication (2FA) is a security mechanism that requires users to provide two separate authentication factors to verify their identity before gaining access to an account or system. These factors typically fall into three categories:

Something You Know: This is typically a password or PIN that the user knows.

Something You Have: This can include a physical device like a smartphone, smart card, or security token.

Something You Are: This relates to biometrics, such as fingerprint scans, facial recognition, or iris scans.

2FA adds an extra layer of security beyond passwords. Even if an attacker manages to obtain your password, they would still need the second factor to gain access. This makes it significantly more challenging for unauthorized users to breach your accounts.

Monitoring Data Breaches

Monitoring data breaches is a critical aspect of cybersecurity, and AI-driven tools like PowerDMARC’s DMARC XML reader provide granular insights into your email sending sources. This tool is designed to act as a vigilant sentry, tirelessly guarding against email-based threats 24/7.

PowerDMARC’s AI-based threat detection service employs specialized algorithms powered by artificial intelligence to perform in-depth analysis and monitoring of email traffic. One of its key functions is to rapidly identify the global blacklists on which each IP address is located. This is crucial because IPs on blocklists are often associated with spam, phishing, or malicious activity. Detecting such IPs helps prevent potentially harmful emails from reaching your inbox.

The engine assesses the email reputation of sending hostnames. This reputation analysis helps identify whether a sender’s domain is known for sending legitimate emails or has a history of sending spam or malicious content.

Wrap-Up

In 2023, AI has continued its transformative impact on cybersecurity, enabling organizations to adapt to the evolving threat landscape and bolster their defenses against cyberattacks. With tools like advanced threat detection, behavioral analytics, AI-enhanced authentication, AI-powered threat detection, and more, AI has proved its potential in the cybersecurity landscape. Beyond cybersecurity, many are also exploring AI-driven side hustles to leverage this technology for innovative business opportunities.

However, it’s important to stay updated on the latest developments and best practices in AI cybersecurity to effectively protect against emerging threats. To learn more, contact us today!

About
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Ahona Rudra

Domain & Email Security Expert at PowerDMARC

Ahona is the Marketing Manager at PowerDMARC, with 5+ years of experience in writing about cybersecurity topics, specializing in domain and email security. Ahona holds a post-graduation degree in Journalism and Communications, solidifying her career in the security sector since 2019.

How to Protect Your Passwords from AI