AI-driven assaults evolving, with malware and deepfakes leading the charge in transformation
In the ever-evolving landscape of cybersecurity, a new threat is looming: the integration of generative AI. This powerful technology is being harnessed by attackers to automate and improve their operations, creating a cat-and-mouse game with security experts.
Attackers are now using AI to create fake open-source utilities, tricking developers into unknowingly incorporating malicious code into their applications. This sneaky tactic could backdoor code before it even hits production, making it a significant concern for corporate stakeholders.
One area where AI is making a significant impact is in social engineering and attack automation. Generative AI enables the creation of highly sophisticated phishing attacks that are free from typical spelling and grammar mistakes, making them much harder to detect than traditional phishing. These attacks can be scaled up massively and personalized quickly to evade established security systems.
AI is also being used to create deepfakes and voice cloning. Attackers use these to impersonate trusted people, requesting money or sensitive information fraudulently. This exploits social engineering by making fake communications appear authentic and familiar to targets.
In addition, generative AI is assisting hackers in creating malware that frequently changes its structure (polymorphic malware) to evade signature-based detection systems. It can also automate the production of zero-day exploit scripts and lateral movement tools within networks, increasing attack sophistication and volume.
Hackers are also manipulating AI models themselves using advanced techniques such as data poisoning, model extraction, and membership inference. These attacks undermine AI-based defenses and compromise model integrity.
Criminals are also exploiting weaknesses in generative AI reasoning models to steal sensitive information or coerce the AI into producing malicious content. For example, "Link Traps" use generated responses containing disguised malicious URLs to covertly exfiltrate user data.
Moreover, hackers are using accessible offline generative AI tools to automate and improve phishing, malware creation, and other cyberattacks. Unlike cloud-based AI with restrictions, offline AI has fewer controls, enabling faster and more frequent attacks by even less-skilled criminals.
The latest novel attack techniques introduced by the integration of generative AI in hacking operations are a cause for concern. Defenders must respond with AI-aware threat detection, continuous training, multi-layered security, and strict data integrity monitoring to mitigate these evolving risks.
Recent research by HP has reported that hackers had used AI to create a remote access Trojan. This serves as a stark reminder of the urgent need for organizations to stay vigilant and proactive in their cybersecurity measures.
[1] https://www.forbes.com/sites/stevemorgan/2021/05/03/artificial-intelligence-is-revolutionizing-the-threat-landscape-heres-how/?sh=6a6617c74f80 [2] https://www.ibm.com/think/artificial-intelligence/ai-adversarial-attacks/ [3] https://www.sciencedirect.com/science/article/pii/S2405452320300622 [4] https://www.techrepublic.com/article/how-ai-can-help-or-hinder-cybersecurity/ [5] https://www.wired.com/story/ai-phishing-scams-deepfakes-security-threats/
- The integration of generative AI in hacking operations is a new risk for corporate stakeholders, as attackers are now using AI to automate the creation of malicious code that can be unknowingly incorporated into applications, potentially backdoor code before it even hits production.
- In the realm of social engineering and attack automation, generative AI enables the creation of highly sophisticated phishing attacks that are free from spelling and grammar mistakes, making them difficult to detect and posing a significant threat to data privacy.
- Hackers are also using generative AI to create polymorphic malware, which frequently changes its structure to evade signature-based detection systems, and to automate the production of zero-day exploit scripts and lateral movement tools within networks, increasing attack sophistication and volume.
- Furthermore, attackers are exploiting weaknesses in generative AI reasoning models to steal sensitive information or coerce the AI into producing malicious content, which underscores the need for strict data-and-cloud-computing security measures in cybersecurity risk management.
