What is Dark AI?

In what reads like the summary of an ever-escalating arms race, the CEO of NVIDIA recently predicted that, as artificial intelligence (AI) is increasingly able to produce fake information at high speeds, defenders will have to match that speed using their own AI tools. Is he correct? Are AI adversaries inevitable? Just what are the “dark AI” tools they use? And are there actionable ways to counter attackers wielding the power of AI? We’re breaking it down.

What is Dark AI?

Dark AI refers to the malicious use of AI in cybersecurity, but also the manipulative and unregulated use of AI technology that poses a significant risk to organizations. It includes:

Cyber Attack Uses

• Phishing scams and social engineering
• Deepfake technology, either audio or visual
• AI used to create malware that adapts itself to avoid detection
• AI-driven vulnerability scanning to find targets for attack
• AI-generated ransomware
• AI-powered automation to scale attacks like credential stuffing attacks

Manipulative Uses

• AI used for manipulative purposes, as in apolitical or predatory advertising or social media campaigns.
  • Generative AI, including large language models (LLMs) like ChatGPT, can create thousands of scripts instantly, making short work of creating highly slanted content. 
  • Tools like FraudGPT and WormGPT are tailored for cybercrime content production and can be found on platforms like Telegram.
  • Chatbot attacks can unleash thousands of accounts into an ecosystem, skewing conversations and sowing division and misinformation.
• AI models with built-in bias trained on flawed datasets, especially used in decision-making in fields like insurance, policing, hiring, or lending

Unregulated Uses

• Autonomous AI systems that operate without oversight
• Opaque AI learning models with decision-making abilities that aren’t clear
• Private sector AI use where AI use may fall into a regulatory gap
• AI without complete testing and validation
• AI that uses insufficient data, or data without consent

We’ll focus on combating dark AI in cybersecurity, but increasingly, the principles that secure organizational assets become tools to improve AI use across organizations.

What are Common Dark AI Attack Strategies?

The use of AI in cyberattacks is a significant concern for 87% of cybersecurity professionals, though less than half of those feel their current defenses prepare them to counter attacks. 

“Cyber Threats are evolving, like the threat landscape is evolving, the amount of exposure that you have is probably greater than it’s ever been before. People are being asked by regulators, by the board, by, you know, their own business continuity needs to be more secure… it’s not about a firewall or passwords anymore, right? It’s about ensuring that you won’t have an embarrassing breach.”

— Joshua Burgin, CPO, Upwind

Preparedness starts with a survey of the landscape, so let’s get an in-depth look at what common dark AI attack strategies look like.

AI-Powered Phishing Attacks

Dark AI has automated phishing campaigns, with personalized attacks conducted at scale. With machine learning algorithms, cybercriminals can analyze vast amounts of personal data, from social media profiles to company biographies, to craft highly accurate, pointed emails that are more likely to deceive victims.

How does it work? Attackers can generate deepfake voice recordings of company executives to create phishing attempts in emails that look and sound legitimate. The AI-driven attack is difficult to detect since it mimics a legitimate company leader.

AI-Driven Malware Creation

Dark AI can create and deploy adaptive malware that evolves based on its environment, using machine learning to analyze the responses it gets from target systems and alter its behavior to avoid detection.

Adaptive malware bypasses signature-based detection by changing its code every time it infects a new system. These AI-driven malicious code-based viruses can detect the type of antivirus software they encounter and behave accordingly, learning from the defenses they face what kinds of evasive measures will work.

AI-Powered Exploit Discovery

Dark AI automates vulnerability scanning in software and hardware systems that attackers want to infiltrate. By analyzing millions of lines of code, Dark AI can quickly identify weaknesses that are ripe for exploitation.

For instance, a Dark AI system could continuously scan software applications for zero-day vulnerabilities and then automatically generate exploits to take advantage of them. They’re likely to be much faster than human teams trying to patch those same vulnerabilities.

Automated Ransomware Generation

Dark AI can help generate ransomware strains that are more sophisticated, adaptive, and difficult to detect. Adaptive ransomware can learn from its environment and stay hidden longer, ultimately exfiltrating greater amounts of data, which can wind up for sale on the dark web. They can also move within systems undetected to critical systems where more damage can be done. It can generate dynamic encryption keys that make it harder for systems to decrypt files without knowing the specific method or key used. It can also target files that are most likely to contain sensitive records, prioritizing those for encryption.

A Dark AI ransomware attack could deploy in a system via phishing attacks, then scan the victim’s system using AI for critical files, encrypting them competently and quickly, and with less chance organizations can undo the damage without paying a ransom.

Automated Brute Force Attacks

AI can increase the speed and efficiency of brute force attacks, attempting large volumes of username and password combinations until they discover a winner. Machine learning can help the process, weeding out patterns and finding weak passwords in credentials.

Many automated brute force attack tools use “password spraying” techniques, testing common passwords across many accounts. These tools can also adjust their tactics, learning from failed attempts and refocusing on weaker credentials and common patterns first for faster success.

Can AI Combat Dark AI?

Yes, AI can combat Dark AI beyond the tit-for-tat escalation that many fear. In fact, AI is one of the most effective tools against the threats posed by Dark AI. 

Why? Its advanced learning capabilities mean it is able to proactively hunt threats before they happen, not just react to them faster. 

But to effectively combat Dark AI attacks from fraud to ransomware, adaptive malware, and phishing attempts, organizations need to deploy AI-driven security systems, leveraging machine learning, deep learning, and other advanced techniques. 

Teams can fight back using multiple types of AI techniques within multiple tool categories, all geared at different types of attacks.

Technique	Primary AI Tool	Tool Category	Core Benefit	Example Use
AI-Driven Threat Detection and Response	Anomaly detection algorithms	Security monitoring (CNAPPs)	Detects suspicious activities (e.g., abnormal login times, compute spikes) in real-time	Detecting runtime anomalies in cloud environments using CNAPPs
Proactive Threat Hunting	Predictive analytics	Threat intelligence (SIEM)	Identifies emerging threats by analyzing past attack patterns	AI simulating DArk AI behaviors in automated red teaming
AI-Powered Malware Detection and Mitigation	Evolving malware detection models	Endpoint Detection and Response (EDR)	Adapts to new malware variants for up-to-date defenses	Detecting evolving malware strains on endpoints
Evading AI-Powered Attacks	Deception technology (honeypots)	Deception technology, like deception networks	Diverts Dark AI attacks away from critical systems	AI-powered honeypots absorbing attack traffic
Automated Threat Response	Automated response systems	Automated incident response (SOAR, often coupled with SIEM data)	Minimizes impact by automating threat containment and system recovery	Quarantining infected systems and restoring safe systems automatically
Counteracting AI-Powered Social Engineering	Deepfake detection tools	Social engineering detection tools	Identifies manipulative content like deepfakes and phishing	AI detecting inconsistencies in voice and audio used in phishing attempts

Here’s what that looks like as teams work on each front to counter attacks.

1. Leverage AI-Driven Threat Detection and Response

Use AI for anomaly detection. Machine learning models can be used to analyze network traffic, system behaviors, and user activities to detect abnormal patterns that could indicate an AI-powered cyberattack. Behavioral analysis sets baseline expectations and flags behaviors that deviate from what’s expected, from late-night logins to unexpectedly high compute use. It’s already at work in CNAPPs that analyze runtimes for anomalous behaviors.

2. Employ AI for Proactive Threat Hunting

AI can be used to predict potential AI-driven attacks before they even occur. By analyzing historical attack data and trends, AI systems can forecast emerging Dark AI threats and implement preemptive measures to stop them before they infiltrate an organization’s systems.

In addition, AI can be used for continuous, automated penetration testing (red teaming), simulating the behaviors of Dark AI and proactively testing an organization’s defenses. This helps uncover weaknesses and vulnerabilities in systems that malicious AI could exploit.

3. Use AI-Powered Malware Detection and Mitigation

AI can be trained to detect and neutralize AI-generated malware that evolves over time. As Dark AI adapts to bypass security systems, AI-based detection tools can continually learn from new attack techniques and evolve their own defenses, catching up with these shape-shifters.

In the case of an AI-driven malware attack, AI systems can autonomously respond by containing the threat, isolating infected systems, or even patching vulnerabilities that were exploited by Dark AI.

4. Evade AI-Powered Attacks With AI Defenses

Just as Dark AI may use evasive techniques to avoid detection (e.g., polymorphic malware), AI can use countermeasures to mislead or deceive the malicious AI. For example, organizations can introduce “decoy” or “honeypot” systems into the network that appear to be legitimate targets. These fake systems will absorb the attack, allowing defenders to analyze the Dark AI tactics without compromising real assets.

5. Automate Threat Responses Using AI

AI can automate the response to Dark AI threats in real time, ensuring that even if an attack is detected late, the damage is minimized. Automated systems can immediately quarantine malicious processes, block malicious network traffic, and even roll back compromised systems to a known safe state. And they continuously improve their defenses by modifying firewall rules, updating detection algorithms, and patching vulnerabilities.

6. Counteract AI-Powered Social Engineering

Dark AI can be used for sophisticated social engineering attacks like creating deepfakes or phishing emails. AI can be trained to spot these attacks by analyzing patterns in text, voice, and video, identifying inconsistencies that indicate manipulation.

Ultimately, AI will inevitably be a central weapon against Dark AI. It’s uniquely suited to combat the evolving, adaptive nature of Dark AI, with the speed and assurance of real-time detection, predictive capabilities, and automated responses alike. 

Upwind Harnesses Machine Learning for Superior Threat Detection

To catch sophisticated dark AI threats, behavioral analysis at runtime is key. With pattern recognition, anomaly detection, and adapted learning, AI works to identify not just known, signature-based threats but zero-days and insider threats, too. It allows for real-time isolation of systems, blocking malicious traffic and preventing damage before it escalates. 

Want to see how behavioral analysis based on machine learning elevates your security against Dark AI threats? Schedule a demo.

FAQ

What does AI detection look for?

AI detection looks for patterns that deviate from normal system behaviors that machine learning establishes after observing systems. It looks at user actions, system behaviors, and network traffic to detect activities that may signal malicious activity. Some examples include:

• Unusual login times and locations
• Suspicious file access or changes to sensitive data
• Abnormal traffic patterns like unexpected data transfers
• Unauthorized privilege escalation or misuse of access rights
• Excessive resource use, like CPU and memory spikes

How does Dark AI differ from traditional AI?

Dark AI uses the same machine learning technology for malicious purposes. While traditional AI is used to improve efficiency and problem-solving, AI is designed to cause harm and evade detection. Besides its core purpose and use, Dark AI can also differ in the following ways: 

• It can be less transparent, operating in secrecy without monitoring or guardrails on ethical use.
• It can be designed to adapt to security systems to avoid detection, rather than to collaborate with other systems it encounters.
• It operates without regulation, outside of legal guidelines.

How is Dark AI evolving?

Dark AI is getting darker as it ingests more data at higher rates, learning from its previous exploits. It has developed the ability to adapt its behavior to avoid traditional security measures, changing its attack measures to meet stronger defenses from its targets. It also automated the scaling of attacks, such as credential stuffing, so it can target millions of accounts simultaneously for more successful attacks with less human intervention. 

As Dark AI gets better, it can also craft more convincing phishing and social engineering attacks, using voice, video, and text that mimic humans and personalize attacks. In short, Dark AI is getting more complex alongside all AI models. For Dark AI, that has meant the increasing success of its tactics, making them more relevant to targets and more effective at evading defenses.

What makes Dark AI tools dangerous?

Dark AI tools are dangerous in that they can operate outside regulatory guardrails and ethical standards. They’re specifically designed to exploit vulnerabilities and automate attacks, so they enable cybercriminals to operate at scale and with more specificity and precision than ever before. Dark AI has become:

• Automated
• Operationalized at scale
• Stealthy to avoid detection
• Precise and personalized, based on public profiles and vast amounts of data
• Highly sophisticated, as with malware that changes its own structure to avoid detection

It’s all made Dark AI harder to counter, and therefore, more dangerous.