Dark AI: Exploring the Shadows of Artificial Intelligence
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What is dark AI?
Dark AI is the malicious use of artificial intelligence to develop cyberattacks, commit fraud, or execute other illicit activities. It is also on the rise. Generative AI (GenAI) has lowered the barrier to entry, making it easier for cybercriminals to design and launch attacks.
At the same time, the rapid adoption of enterprise technologies, including AI, has expanded organizations’ digital attack surface, introducing vulnerabilities that threat actors can exploit.
By 2025, cybercrime is expected to cost $10.5 trillion annually, and a growing share of that will be driven by dark AI.
Defining dark AI
Dark AI is not a specific technology; it’s a set of tactics and behaviors that exploit AI’s capabilities for harmful ends. Key characteristics include:
Anonymity: Attackers operate in hidden online spaces, often using encryption, referrals, and cryptocurrencies to avoid detection.
Autonomy: Dark AI systems often run on autopilot with minimal human input, enabling attackers to launch large-scale attacks targeting multiple victims simultaneously.
Adaptability: Adversaries continuously adopt the latest AI innovations and adjust their methods to evade new detection systems.
Human-like behavior: From deepfakes to persuasive social engineering, dark AI mimics real human interactions to deceive and manipulate.
Whereas traditional AI is designed to improve productivity, decision-making, or quality of life, dark AI is built to infiltrate, mislead, and cause harm. It exploits trust and obscures intent, often with devastating consequences.
Dark AI challenges and cybersecurity threats
The rise of AI has made it faster and easier for attackers to launch sophisticated cyberattacks. What once required deep technical expertise is now accessible to a much wider range of threat actors, and many are using AI to sharpen their tactics and outpace traditional defenses. Understanding how dark AI accelerates risks can help teams stay proactive and resilient:
AI-powered cyberattacks: Ready-to-use toolkits available on the dark web make it easy for attackers to launch phishing, malware, and ransomware campaigns. As a result, the volume and scale of attacks have surged.
Evasion capabilities: Machine learning models help cybercriminals adapt to security systems in real time, making detection harder. Attack patterns can shift automatically to avoid known defenses.
Speed of attacks: AI compresses the lifecycle of an attack. Breaches happen faster, and attackers can move across systems before security teams even spot the intrusion.
A recent Microsoft report revealed an alarming uptick: the number of nation-state and financially motivated threat actors tracked by the company has jumped from 300 to 1,500. Meanwhile, password attacks have grown from 579 per second in 2021 to over 7,000 per second in 2024. In many cases, attackers equipped with AI can gain access to enterprise systems just over an hour after a malicious link is clicked.
AI enables advanced attack strategies
In addition to creating new threats, AI makes familiar attack strategies more dangerous and harder to detect. Cybercriminals can use AI to streamline old tactics and increase their success rates across a broader range of targets:
Malware generation: AI can help attackers build malware that evolves over time, learning how to bypass security controls and rendering some traditional antivirus tools far less effective.
Phishing automation: Generative AI tools allow adversaries to create convincing phishing emails, texts, and voice messages, customizing campaigns from templates.
Optimized ransomware delivery: Dark AI can personalize ransomware attacks, automate steps, and optimize attacks for specific systems.
Social engineering: By analyzing a victim’s online behavior and communication patterns, AI helps attackers tailor social engineering tactics to appear more authentic, making it harder for users to spot suspicious activity.
Dark AI tools in the wild
Threat actors today have access to preconfigured tools that make it easier to launch sophisticated cyberattacks. Some are custom-built for cybercrime, while others are open source tools repurposed for malicious use. These include:
FraudGPT: A low-cost subscription-based GenAI tool that automates the creation of malicious code, malware, and phishing emails, helping attackers find vulnerabilities and compromised sites.
DarkBART and DarkBERT: DarkBART is based on Google Bard and is capable of creating targeted attacks. DarkBERT comes in two versions: One is based on a legitimate cybercrime tool, while the other leverages the dark web as its training data to exploit vulnerabilities.
Open source tools: Hostile parties use Nmap, Shodan, Metasploit, SQLMap, and other open source tools to conduct reconnaissance, exploit vulnerabilities, and create new malware and phishing campaigns.
Real-world examples of dark AI attacks
Organizations are already seeing the real-world consequences of dark AI, from targeted data breaches to financial fraud. Here are a few recent examples that show how attackers are using AI to do their bidding:
Deepfake fraud: In one noteworthy case, an attacker used deepfake technology to impersonate the CFO of a financial services company. They convinced an employee to transfer $25 million to fraudulent accounts.
Phishing attack on Activision: An SMS phishing attempt targeted a privileged user at Activision, aiming to steal sensitive internal information. While the company denied any significant breach, security researchers highlighted how social engineering attacks, enhanced by AI, are becoming harder to spot and prevent.
T-Mobile espionage: A state-sponsored group exploited vulnerabilities in T-Mobile’s infrastructure, accessing call records, unencrypted messages, and audio communications from targeted individuals.
Data privacy at risk
Dark AI threatens the very foundations of data privacy. Using sophisticated attacks, threat actors can access personal and sensitive data (e.g., credit card numbers, health records, and private communications). This data is then weaponized in secondary attacks, such as identity theft, extortion, and financial fraud.
Attackers also work hard to stay hidden, using tactics like spoofed IP addresses, VPNs, and proxy networks to avoid detection. These methods can make it harder to enforce even the strictest data protection frameworks, such as GDPR, HIPAA, and PCI-DSS.
Building strong, proactive data protection measures such as data encryption, access controls, and real-time monitoring is key to minimizing the impact if attackers manage to get through.
Exploiting social and communication platforms
Social media and communication platforms are increasingly being used as entry points for AI-driven scams. Attackers impersonate executives on LinkedIn and Twitter, mine Facebook and Instagram profiles for personal details, and even manipulate public discourse through deepfakes and AI bot activity.
By crafting more convincing messages and gathering more personalized data, AI helps attackers make scams harder to detect, and makes defending brand trust and user privacy even more important. Ongoing employee awareness, stronger identity verification processes, and smart monitoring of public-facing channels can help organizations reduce the risks tied to AI-powered social engineering.
Broader implications of dark AI
The impacts of dark AI go far beyond individual companies. As AI-generated communications become harder to detect and attacks become more personal and targeted, the broader effects on trust, communication, and decision-making are becoming more visible. Beyond protecting their own systems, organizations working against dark AI play a role in maintaining trust across entire industries and communities. Broader risks of dark AI include:
Impersonation of authority figures: Adversaries can impersonate company leaders or peers, creating confusion and mistrust inside organizations.
Erosion of truth: The rise of AI-generated media makes it increasingly difficult to distinguish between legitimate content and malicious material.
Loss of public trust: Organizations may lose the trust of customers, partners, and the public if they become embroiled in AI-driven scams or data breaches.
How to protect against dark AI
Staying protected from dark AI and other emerging cyber threats requires the integration of proactive, resilient security practices into every layer of your organization. By combining smart technology, ongoing education, and a strong security foundation, teams can reduce risks without slowing innovation. Here are some tips for staying ahead:
Track emerging threats
Stay informed about new AI-powered attack methods, tools, and tactics. Threat intelligence feeds, community updates, and industry reports can help your teams anticipate how attackers might use new technologies.
Train employees
Empower your teams to recognize and respond to AI-enhanced phishing attempts, deepfakes, and social engineering. Regular training builds a culture of security awareness and can turn employees into an effective first line of defense.
Enhance security with AI
While it is introducing new dangers, AI can also be a force for good. Use advanced detection tools that can spot anomalies, detect zero-day threats, and automate response actions. Security solutions like Snyk can also help you shift left by embedding security into development workflows, so vulnerabilities are caught and fixed as early as possible.
AI red teaming
To counter these sophisticated threats, security teams can think like their adversaries. AI red teaming is a proactive security measure where ethical hackers use AI to simulate realistic attacks on an organization's systems, applications, and data. This goes beyond traditional red teaming by leveraging AI tools to automate reconnaissance, exploit vulnerabilities, and navigate complex networks, mirroring the tactics of modern attackers.
By using AI to probe for weaknesses, organizations can identify and patch vulnerabilities before malicious actors can exploit them. The insights gained from these simulations are critical for building more resilient defenses and ensuring a "secure by design" posture. It's a step in preparing for a future where AI-powered attacks are the norm.
How Snyk can help
As attackers use dark AI to move faster, organizations need to match that speed with proactive, developer-first security practices. That's where Snyk comes in. By helping teams embed security early into the development lifecycle, Snyk makes it easier to catch vulnerabilities no matter how they’re introduced.
With Snyk Code, powered by DeepCode AI, developers can automatically find, fix, and prioritize vulnerabilities exponentially faster than manual reviews. Built for modern development workflows, Snyk empowers teams to move quickly without sacrificing security.
The rise of Dark AI means that attackers are not just faster, they’re smarter. Interested in seeing how AI red teaming can help you get ahead of the next attack? Read more about how to red team your LLM agents.
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