The Dark Side of AI: How Hackers Use AI for Cybercrime
AI’s Double-Edged Sword: Dark Side of AI in Cybersecurity
AI technology continues to evolve rapidly while serving as an essential tool for cybersecurity protection and a dangerous instrument for black hat hackers.
The dual nature of AI in the cyber world becomes evident because its protective applications face threats from malicious activities that exploit the same technology.
The negative aspect of AI becomes evident because it accelerates black hat operations which enable cybercriminals to execute sophisticated attacks at increased speed and broader scope.
The combination of deepfake technology with AI automation enables phishing attacks to create realistic fake identities that deceive human victims.
The timeline in [cited] demonstrates the expanding danger by showing how electric vehicle charging systems and standard cybersecurity systems face increasing vulnerability.
The analysis demonstrates the critical need to fully comprehend AI impacts because both defensive and offensive actors must understand this complex domain to achieve cybersecurity superiority.
How Hackers Use AI for Cyber Attacks?
Artificial intelligence has transformed the methods through which cyber attacks occur.
Machine learning and complex algorithms enable hackers to develop phishing attacks which become more sophisticated and operate at increased speeds. AI technology generates realistic deepfake audio and video content that enables hackers to execute social engineering attacks on people during critical moments.
The technologies enable hackers to replicate typical user behavior which enables them to bypass security systems while performing their operations with greater stealth.
AI systems enable hackers to detect zero-day vulnerabilities at a speed that surpasses human capabilities for exploitation. The evolving nature of threats because of AI capabilities requires immediate implementation of enhanced cybersecurity measures because traditional security systems become less effective.
The visual flow diagram illustrates how AI tools enable hackers to perform automated breaches through automated attacks. [cited]
Percentage of Ransomware Attacks Using AI | Source |
80% | MIT Sloan |
90% | SoSafe’s 2025 Cybercrime Trends Report |
90% | SoSafe’s 2025 Cybercrime Trends Report |
75% | Deep Instinct’s 2024 Report |
85% | Deep Instinct’s 2024 Report |
442% | CrowdStrike’s 2025 Global Threat Report |
135% | Darktrace’s 2023 Report |
AI-Powered Cyber Attack Statistics
Artificial Intelligence Cyber Risks : Risks of AI in Cyber Attacks
New technologies in the cybersecurity domain continuously transform the field through both defensive advantages and offensive vulnerabilities.
The implementation of AI systems has strengthened security measures but simultaneously enabled hackers to execute intricate cyberattacks more efficiently.
The fast-paced nature of AI-driven sophisticated attacks makes it challenging for traditional security systems to maintain their defenses.
The implementation of AI technology creates security risks which start with basic AI systems and extend to the most advanced form of artificial general intelligence (AGI).
The combination of three major security concerns includes malicious attacks and system failures and unanticipated system behavior.
AI systems which provide real-time defense monitoring enable hackers to change their attack methods quickly.
The quick adaptation of hacker tactics creates an environment where security systems face challenges to identify and stop attacks.
The electric vehicle charging vulnerability timeline shows that these security threats need immediate response and complete cybersecurity plan redesigns to defend against AI-based attacks.
Statistic | Value |
Percentage of organizations experiencing AI-based breaches in 2025 | 29% |
Percentage of cybersecurity leaders acknowledging the need for major changes to cybersecurity strategies due to AI threats | 67% |
Percentage of organizations encountering deepfake attacks through social engineering or automated process exploitation | 62% |
Percentage of organizations reporting voice biometric spoofing using deepfake audio | 32% |
Percentage of cybersecurity professionals concerned about future AI-driven threats such as AI-driven phishing and deepfakes | 60% |
Percentage of organizations that feel very well prepared to combat high-volume AI-powered bot attacks | 20% |
Percentage of organizations that experienced breaches due to unauthorized AI tools (‘shadow AI’) in 2025 | 20% |
Average additional cost added to a data breach due to ‘shadow AI’ | $670,000 |
Percentage of AI-related breaches involving AI-generated phishing attacks | 37% |
Percentage of AI-related breaches involving AI-generated deepfake attacks | 35% |
AI-Powered Cyber Attack Statistics and Risks
AI Powered Black Hat Hacking
The implementation of automated systems enables threat actors to launch more attacks. The process of identifying targets and trapping victims depends on generative artificial intelligence systems used by cybercriminals.
AI systems help cybercriminals locate employees who possess valuable information and their specific vulnerabilities.
The technology detects network and system and software vulnerabilities at a rate that exceeds manual threat actor capabilities.
Security experts at Black Hat USA 2025 in Las Vegas brought together professionals to study criminal patterns and develop solutions for organizations dealing with advanced threats.
Understand Threat Actors’ Objectives
Ransomware rises. Zscaler cloud security systems detected 146% more ransomware attacks throughout 2025 against businesses from all industries compared to the previous year according to its “ThreatLabz 2025 Ransomware Report.” The researchers discovered an unexpected pattern during their investigation of these cyberattacks.
The research indicates ransomware operators now choose data extortion over encryption because their data exfiltration activities have increased by 92.7% throughout the previous year.
Organizations face ransom demands from threat actors who have progressed to this new method of coercion. Aamir Lakhani from Fortinet stated that hackers operate like businesses and financial gain drives most cyberattacks according to IT workers.
FortiGuard Labs reports financial motives behind 70% of all detected attacks according to Aamir Lakhani. The threat actors we study possess CEOs and CFOs as part of their organizational structure.
Shannon Murphy from Trend Micro Global Security and Risk Strategy department believes AI requires AI to defeat it effectively.
The pace and scale of AI operations require organizations to maintain equal speed according to her statement. IT executives need to detect the AI-based attacks which threat actors will launch first.
146% The year-over-year increase in ransomware attacks blocked by Zscaler
Source: Zscaler, “ThreatLabz 2025 Ransomware Report,” July 2025
Cybercriminals Work Smarter, Not Harder
The implementation of AI technology enables threat actors to execute multiple types of attacks against organizations.
Lakhani explained that hacker forums now distribute unrestricted large language models and successful hacking programs to their users.
The author conducted tests on multiple models which he described in his report. The AI system processed my request to generate code for exploiting a known zero-day vulnerability.
The LLM succeeded in this task according to him because most commercial models fail to perform this function.
The exploitation of industrial vulnerabilities by threat actors has become more effective through their actions. The digital transformation of energy and agriculture sectors makes these industries more vulnerable to cyber threats.
The attackers focus on disrupting supply chains instead of focusing on individual business entities. The main goal of these attacks is to trigger destructive effects throughout the entire supply chain network according to Zscaler CSO Deepen Desai.
The exploitation of an AI vendor by attackers enables them to launch downstream attacks against all organizations which depend on this vendor. The resulting impact on downstream operations becomes extremely significant.
Attackers use file-sharing applications to achieve the highest possible return on investment. The attackers gain access to all application data after successfully exploiting these systems.
The number of organizations affected by data theft reaches into the hundreds or thousands based on the number of organizations using these applications according to Brett Stone-Gross who leads threat intelligence at Zscaler.
The attackers can obtain massive data sets at once and link multiple zero-day vulnerabilities which proves effective for their operations.
Organizations must implement should always maintain a proactive security posture.
The methods for risk remediation have transformed completely since the better file-sharing permissions and third-party data protection measures to stay ahead of AI-powered threat actors but they security practices of five years ago according to Murphy.
The use of AI for social engineering attacks has evolved into more specific and targeted methods that organizations need to protect against. Organizations face new social engineering threats which extend beyond the creation of sophisticated AI-generated emails.
Attackers use LinkedIn to discover specific job-related email addresses that belong to organizations. Murphy explained that automated reconnaissance enables fraudsters during a red team test by analyzing her visited locations and contact network.
She observed that AI systems have a special interest in content. The situation lacks victim blame because the attack is flawless.
The protection to create customized attacks against their targets. The AI system used her LinkedIn activity to create social engineering attacks of these threats requires IT specialists at firms to take action.
According to Murphy it is our duty to protect our personnel from harm. The technology should handle most of the work according to Murphy.
Cybersecurity executives at all levels need to develop their capabilities for the future.
Lakhani explained that his team analyzed the research methods used by attackers during their operations. The same approach should be applied to discover vulnerabilities which would enable us to fix them according to Lakhani.
The back-and-forth nature of this game continues because it operates as a continuous cycle of cat and mouse.
The reconnaissance-based social engineering attacks enable threat actors to enhance their deepfake technology capabilities. According to Murphy the prevalence of audio deepfakes has reached a high level.
Personnel protection against deepfake attacks requires technology teams to implement detection systems. The endpoint-based solutions use signal detection to identify AI-generated audio and video scams.
Murphy explained that perfect audio quality serves as a warning sign for potential attacks.
The HVAC system produces white noise sounds that I can hear. A single indicator does not indicate dangerous actors but multiple indicators together might suggest their presence.
Digital Twins Enhance Security Audits
Digital twins represent an emerging business solution that continues to grow in popularity. The technology originally served smart cities and industrial applications but IT teams now use AI to create digital twins for red teaming activities.
The business restrictions during red team and penetration tests limit my ability to attack critical assets and vital servers according to Murphy.
The complete digital environment duplication enables us to create authentic attack simulations which meet both business requirements and security leadership expectations. [Link1]
AI in Hacking and Cybersecurity Threats
AI technology in cybersecurity has introduced dual benefits for hackers who obtain advanced tools to perform intelligent password attacks and create deepfakes for social engineering while defenders gain access to threat detection systems and predictive analytics and security automation and automated incident response capabilities.
The dual application of AI technology in cybersecurity produces an evolving threat environment which demands organizations to maintain active development of AI-based security solutions for protecting against new cyber threats.
How AI Empowers Hackers:
- Enhanced Social Engineering: AI systems generate authentic-looking phishing emails and deepfakes through analysis of target digital information to create more believable attacks.
- Automated Password Attacks: AI algorithms perform fast password testing across extensive combinations of breached wordlists and their derivatives which enhances the speed and effectiveness of password guessing and spraying attacks.
- AI Agents as Threats: AI agents deployed inside organizations without proper oversight create substantial security risks because they gain access to essential resources and sensitive information through their identity.
- Scalable Cybercrime: Attackers use conversational AI and chatbot scams to interact with multiple targets while automating their conversations which enables them to expand their operations without human involvement.
How AI Strengthens Cyber Defenders:
- Advanced Threat Detection: AI systems process enormous data sets at high speed to find hidden security indicators which help identify potential intrusions before they become major issues.
- Predictive Analytics: AI systems analyze previous attack data to forecast upcoming cybersecurity threats and detect potential vulnerabilities before they become accessible to attackers.
- Security Automation: AI systems execute repetitive security tasks which enables human analysts to concentrate on advanced strategic work.
- Reduced Human Error: AI systems perform automated tasks which eliminates human involvement from security operations thus decreasing the number of human mistakes that occur.
- Improved Incident response: AI systems use real-time threat analysis to produce immediate response recommendations which enable organizations to handle security incidents with speed and precision.
The Evolving Landscape:
- A Dynamic Cat-and-Mouse Game: The continuous fight between attackers and defenders who use AI technology results in an escalating competition because each side needs to develop countermeasures that match the other’s advancements.
- Increasing Sophistication of Attacks: The implementation of AI technology in cyberattacks results in more intricate and widespread attacks which exceed the capabilities of current security systems to manage effectively.
- Organizational Preparedness: Organizations lack sufficient capabilities to defend against advanced AI-generated threats which creates an expanding security readiness deficit. [Link2]
Dangers of AI in Cybersecurity
AI systems face multiple security threats which include AI-generated phishing attacks and deepfakes alongside adversarial attacks and data poisoning incidents and model theft risks and insecure AI supply chain vulnerabilities.
The deployment of AI systems generates multiple risks which include biased operations and ethical problems and excessive dependence on AI systems and unclear system operations that threaten digital infrastructure stability.
Risks related to AI-powered attacks
- Deepfakes and impersonation: AI technology enables the development of authentic-looking fake content including audio and video and text which enables attackers to successfully carry out social engineering and phishing schemes.
- Automated malicious code generation: AI technology enables attackers to produce advanced malware and malicious code at high speed which reduces cybercriminal entry points and enables massive complex cyberattacks.
- AI-driven disinformation campaigns: AI systems enable users to produce extensive disinformation campaigns which make it difficult to identify genuine information from fabricated content.
Risks to AI systems and data
- Adversarial attacks: The incorrect functioning of AI systems occurs when attackers modify input data to produce wrong decisions or misidentify information which results in major damage to healthcare and financial systems.
- Data poisoning: Attackers who modify training data for AI models result in performance degradation and biased results and potentially dangerous model behaviour.
- Model theft and reverse engineering: AI models face security threats because attackers try to steal intellectual property from them while working to understand their system weaknesses.
- Insecure supply chains: Third-party suppliers provide essential components to AI systems which creates security risks because attackers can embed malicious code or backdoors into these components.
- Sensitive data disclosure: The extensive data requirements for AI system operation and training create major privacy risks because they increase the chances of data breaches and unauthorized access which result in privacy violations and regulatory fines.
Ethical and systemic risks
- Bias and discrimination: AI systems acquire existing biases from their training data which leads to unfair outcomes that maintain social inequalities.
- Lack of transparency and accountability: AI algorithms that are difficult to understand create uncertainty about their decision-making methods which leads to reduced trust and accountability in systems.
- Overreliance on AI: AI systems become dangerous when humans fail to monitor them properly because they lack understanding of AI operations and because AI systems can produce unexpected results.
- Prompt injection: Attackers use prompt manipulation to force AI systems into revealing confidential data while bypassing security measures and executing unintended operations. [Link3]
Conclusion
The development of artificial intelligence in cybersecurity brings new security risks that organizations must address.
The use of AI in cyberattacks has increased because deepfake scams and sophisticated phishing attacks have become more prevalent in the digital world.
Black Hat Hackers Using AI
Black hat hackers leverage AI technology to enhance their operational speed and effectiveness which outpaces the ability of traditional security measures to protect against emerging threats.
The rapid expansion of electric vehicle charging system security threats demonstrates how fast cyber threats can escalate.
Organizations must implement hybrid defence systems which combine human expertise with AI technology to stay protected against evolving cyber threats. Organizations can develop defence strategies against AI-based cyberattacks by using this approach to protect their systems from new attack methods.
Organizations must implement multiple security layers to maintain their position against advancing threats while protecting their data from upcoming cyber-attacks.
Image1. Timeline of Cybersecurity Events Affecting EV Charging Stations (2018-2023)
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