Hacking ANY AI System With JUST One Prompt

The challenge: an analyst’s nightmare

XLoader has been evolving since 2020 as a successor to the FormBook malware family. It specializes in stealing information, hiding its code behind multiple encryption layers, and constantly morphing to evade antivirus tools and sandboxes.

Traditional malware analysis is slow and manual—requiring experts to unpack binaries, trace functions, and build decryption scripts by hand. Even sandboxing (running malware in a controlled environment) doesn’t help much, because XLoader decrypts itself only while running and detects when it’s being monitored, keeping its real code hidden.

This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments, two areas critical to comprehensive cybersecurity assessments. Building on previous research into GenAI’s role in the ethical hacking lifecycle, this paper presents a hands-on experimental analysis conducted in a controlled virtual setup to evaluate GenAI’s utility in supporting these crucial, often manual, tasks. Our findings demonstrate that GenAI can streamline processes, such as identifying potential attack vectors and parsing complex outputs for sensitive data during privilege escalation.

 The study also identifies key benefits and challenges associated with GenAI, including enhanced efficiency and scalability, alongside ethical concerns related to data privacy, unintended discovery of vulnerabilities, and potential for misuse. This work contributes to the growing field of AI-assisted cybersecurity by emphasising the importance of human-AI collaboration, especially in contexts requiring careful decision-making, rather than the complete replacement of human input.

Trend Research uncovered a campaign that uses fake GitHub repositories to distribute SmartLoader, which is then used to deliver Lumma Stealer and other malicious payloads. These repositories disguise malware as gaming cheats, cracked software, and system tools to deceive users.

The campaign leverages GitHub’s trusted reputation to evade detection, using AI-generated content to make fake repositories appear legitimate. Malicious ZIP files contain obfuscated Lua scripts that execute harmful payloads upon extraction.

If the attack succeeds, threat actors can steal sensitive information like cryptocurrency wallets, two-factor authentication (2FA) extensions, login credentials, and other personally identifiable information (PII) that can potentially lead to identity theft and financial fraud.

Cybercriminals are adapting from using GitHub file attachments to creating entire repositories, incorporating social engineering tactics and AI-assisted deception.

Organizations and individuals should adopt proactive best practices, such as downloading software only from official sources, verifying repository authenticity, enabling security tools, and educating users on social engineering risks to mitigate such threats.

Depending on the knowledge of the adversary’s model, white-box and black-box attacks can be performed.

In the simplest white-box case (when the adversary has full knowledge of the model, e.g., a sigmoid function), one can create a system of linear equations that can be easily solved

In the generic case, where there is insufficient knowledge of the model, the substitute model is used. This model is trained with the requests made to the original model in order to imitate the same functionality as the original one.

A number of reasons are contributing to the increase in these attacks. Our increasing use of browsers for work, the proliferation of zero-day exploits, and the sophistication of attackers all fuel the issue. Gen-AI is a critical component, allowing attackers to build sophisticated phishing sites, impersonate AI services, and perform targeted social engineering. Phishing-as-a-service (PhaaS) platforms are also being used by criminals, enabling even less skilled hackers to deliver sophisticated phishing campaigns at scale. On mobile platforms, restricted URL visibility and auto-login capabilities further boost the efficacy of such threats, rendering it even more difficult for users to identify phishing sites.

 By - Aaradhay Sharma