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URL: https://www.trellix.com/blogs/research/the-dark-side-of-innovation-cybercriminals-and-their-adoption-of-genai/
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THE DARK SIDE OF INNOVATION: CYBERCRIMINALS AND THEIR ADOPTION OF GENAI

By Jambul Tologonov and John Fokker · March 06, 2024

In the ever-evolving threat landscape, the Trellix Advanced Research Center has
been at the forefront of understanding and combating the dual-edged sword of
Generative Artificial Intelligence (GenAI). As this technology becomes
increasingly sophisticated, it offers boundless opportunities for innovation and
security. To quote F. Scott Fitzgerald from the Great Gatsby: “The party has
begun.”

It is a fact that any technological advancement will open new avenues for
exploitation, and the same is true with GenAI. Cybercriminals are ‘early
adopters’ of new tech and soon after the ChatGPT launch it was the ‘talk of the
town’ on several cybercriminal fora. Given the LLM capability, crafting phishing
emails indistinguishable from legitimate communications was one of the first
things mentioned in the criminal community and it started from there.

Recently, OpenAI announced it had taken proactive steps to counter the misuse of
their platform by several nation-state affiliated groups. However, this misuse
isn’t limited to nation-state affiliated groups, Trellix has been observing a
growing interest and use of GenAI by cybercriminal actors. In this blog we will
highlight some of the concerning examples we have observed.


AI ASSISTED EXPLOIT DEVELOPMENT

A prominent case of the misuse of legitimate GenAI services recently came to
light through coverage on the Russian cybersecurity website/journal xakep.ru. An
article on the site details how the author exploited a recently disclosed
vulnerability and crafted an exploit with the assistance of ChatGPT-4.

The Xakep.ru, where 'xakep' is a Cyrillic transcription meaning a hacker,
operates on a subscription-based model. The blog post would have likely gone
unnoticed by Trellix if not for an underground threat actor who posted it in its
entirety on a well-known dark-web forum. This post meticulously outlined each
step of the exploitation process for the Post SMTP Mailer WordPress plugin
vulnerability.

The vulnerability in question, CVE-2023-6875, is classified as critical with a
CVSSv3 score of 9.8/10. It was disclosed on January 11, 2024 and affects the
Post SMTP Mailer plugin for WordPress in all versions up to 2.8.7. The xakep.ru
article author not only acknowledges the existence of a Proof of Concept (PoC)
for CVE-2023-6875 but also advises that the PoC, while existing, is incomplete
and cannot be successfully exploited.

In the article, the author demonstrates how the vulnerability can be exploited,
leading to the identification of the SMTP administrator, resetting the
administrator password, and ultimately uploading a zip file containing a web
shell to the WordPress server. According to statistics from wordpress.org,
approximately 150,000 WordPress websites on the internet are potentially
affected by the Post SMTP Mailer plugin vulnerability, including the websites of
media and government organizations.

Figure 1. A screenshot of the xakep.ru article observed in an underground forum.


Towards the end of the article, the xakep.ru author advises that their exploit
dubbed 'boom-boom' is written in the Go programming language with the assistance
of ChatGPT-4. The author goes on to provide a link to the GenAI-based exploit
script and an Imgur video demo. The author emphasizes that one does have to be a
proficient programmer to exploit a vulnerability. By understanding the
vulnerability's principles, they can delegate or outsource the programming part
to GenAI, making the exploitation process more accessible to a wider audience.

Figure 2. GenAI aided exploit written in Go shared on xakep.ru’s Github
repository
Figure 3. A screenshot of the ‘boom-boom’ Go-based exploit written with the help
of GPT-4. Author advises although they are not a developer/programmer that does
not mean they can’t read English and debug the errors.


Unfortunately, the xakep.ru did not delve into the specific details of how
ChatGPT was utilized to assist in writing the exploit code in Go. The
methodology behind leveraging ChatGPT-4 for code generation remains unknown, and
without explicit information, one can only speculate on possible approaches.
Methods such as prompt injection, jailbreaking, or other attack vectors on
ChatGPT could be among the techniques used, but the precise process remains
undisclosed based on the provided information.


DEEPFAKE QUEST FOR NEXT-GEN DECEPTION

Another particularly alarming development is the observation of Russian threat
actors closely monitoring Chinese advancements in AI, specifically in the realm
of deepfake technology. This interest hints at the potential for creating highly
convincing fake content, which could be used in everything from disinformation
campaigns to impersonating individuals in secure communications.

Recently we observed in a Russian speaking dark-web forum thread titled ‘Theory
and practise of creating Deepfake’ threat actors engaged in a discussion around
recent technological development in GenAI-based deepfakes and one of the actors
advised that old technologies of deepfake generation are outdated since the
introduction of InstantID: Zero-shot Identity-Preserving Generation.

InstantID, unveiled by Chinese researchers at Peking University in early 2024
has revolutionized the deepfake landscape. This AI method enables the rapid
creation of highly efficient, identity-preserving images within seconds, based
on a single input image file. InstantID has already been incorporated into a
popular GenAI text-to-image model named Stable Diffusion, along with its webGUI
tool called Automatic1111.

What sets InstantID apart from other models, such as LoRA, is its accessibility
to threat actors with minimal skill sets and hardware resources. With just a
single reference image, individuals can swiftly generate convincing deepfake
content in a matter of seconds. Notably, one Github user has shared examples of
InstantID, providing a glimpse into the capabilities of this groundbreaking AI
technology:

Figure 4. InstantID examples shared on InstantID Github repository


Another example of InstantID’s exceptional results were recently posted on X by
a Chinese academic Wang Jian:

Figure 5. InstantID examples shared on X by @Datou


The capabilities of the InstantID method are indeed impressive, as it can
achieve remarkable results in less than a minute with just a single face
reference. What differentiates InstantID from other techniques is its ability to
generate content without the need for extensive prior training or a lengthy
fine-tuning process. This technology's capability to generate high-quality
outputs with minimal input and time investment underscores its potential impact
in the deepfake domain. However, it also raises concerns about the potential
proliferation of easily created deepfake videos and images used by
cybercriminals. The accessibility and high adaptability of InstantID could
contribute to an increase in the prevalence of such deceptive content, posing
challenges for the identification and mitigation of deepfake digital threats.

At present, InstantID is primarily employed for image generation. However,
threat actors speculate that it is only a matter of days before this AI
technology extends its capabilities to include deepfake video production. This
anticipated evolution may involve the integration of speech-to-speech or
image-to-video AI technologies, showcasing the continuous adaptability and
potential advancements in the realm of AI-driven malicious activities:


Figure 6. A screenshot of underground threat actors discussing InstantID’s usage
in deepfake creation process. First they discuss text-to-speech AI model called
WhisperSpeech, then how to train Deepfacelab given the limited amount of input
photos, and at last the InstanID technique as a replacement of old technologies
for deepfake creation


While researching InstantID technology usage, we came across highly persuasive
celebrity images on underground forums generated using the InstantID +
Automatic1111 AI model, however due their explicit visual nature we will not
reference or include them in this blog.

Another notable example of deepfakes observed in Russian dark-web is a
legitimate AI service called camerai which allows to generate deepfake video
based on a given AI prompt and a live user camera, however at the moment the
quality of the produced deepfake video leaves a lot to be desired:

Figure 7. A screenshot of ‘camerai’ service usage observed on an underground
forum. Cybercriminals advise all you have to do is to write down any character
as a prompt and turn on the camera, the rest is done by the neural network.


Given recent AI advancements like InstantID, the creation of deepfake content
has become more accessible than ever before, and cybercriminals are keenly
observing these developments. As pioneers in adopting this innovation, they
consistently experiment with and integrate deepfake GenAI technology to craft
highly convincing synthetic media. This synthetic content can be employed in
their new fraudulent campaigns, showcasing the adaptability and utilization of
these advanced technologies for nefarious purposes.


AI INTEGRATED WEB-SCANNER

Cybercriminals are consistently advancing and creating new customized
AI-embedded malicious tools to serve their illicit purposes. Trellix has
recently identified such a tool on the dark web, – an automated web-scanner
integrated with AI. This specialized tool functions as a custom web application
vulnerability scanner, generating an AI-based report along with recommendations
derived from the results of well-known penetration testing tools such as Nmap,
Nikto, Sqlmap, SSLScan, DNSRecon, and others. This Python-based web scanner
incorporates Llama API integration, allowing users to tailor AI profiles based
on their preferences, ranging from a friendly mentor to a cyber special agent or
a vigilant expert. The tool offers extensive customization options, enabling
users to add their own extensions, activate/deactivate pentesting modules, and
define their unique prompt style and AI feedback.

Figure 8. A screenshot of a custom AI based web-scanner observed on an
underground forum. Author ‘hackeryaroslav’ presents an automated web-scanner
with AI integration v1.0 designed to discover vulnerabilities in
web-applications.


Here are the Llama AI profiles used in the web-scanner:

 friendly_mentor_profile = AIProfile(                  
       "friendly_mentor",
       "You are a seasoned yet amicable cybersecurity mentor. You explain vulnerabilities and mitigations in a simple, easy-to-grasp manner, like guiding a mentee. Your warmth shows this is for learning, not lecturing." 
)

 special_agent_profile = AIProfile(                  
       "special_agent",
       "You are a cyber intelligence special agent briefing high-level government officials on security threats. You analyze methodically, profiling adversary tradecraft, capabilities, and recommended counter-operations for the targeted organization." 
)

hacker_guru_profile = AIProfile(                 
       "hacker_guru",
       "You're the zen-like hacker guru, seeing vulnerabilities as puzzles to solve over cups of green tea. For each finding, you philosophize on root causes and ponderously guide the grasshopper to patches, wisdom, and improved security hygiene." 
)

paranoid_expert_profile = AIProfile(                 
       paranoid_expert",
       "You're the paranoid cybersecurity expert seeing threats everywhere. Your analysis wildly speculates possible worst-case scenarios from the findings, while your mitigation advice involves heavy-handed measures like air-gapping, encryption, threat hunting operations centers, and resisting use of all technology." 
)

Here is the default Llama AI prompt used which can be customized for the end
user needs:

default_prompt = """                 
       You are a penetration tester and security consultant. The vulnerability scan on [TARGET] has revealed the following findings:
      
       [Tool] findings:
       - [VULNERABILITY_DESCRIPTION]
       - [ANOTHER_VULNERABILITY_DESCRIPTION]

       ...

       No vulnerabilities detected.

       Analyze the identified vulnerabilities and recommend possible variants or scenarios that might lead to additional security issues in the future. Provide insights into potential attack vectors, exploitation techniques, or misconfigurations that could be exploited by malicious actors.

       Consider the currrent security posture and suggest improvements to mitigate the identified vulnerabilities. Your recommendations should foucs on enhancing the overall resilience of the target system.
      
       [USER_PROMPT]
       """
 

The web-scanner’s output report with AI recommendations look as follow:

Figure 9. A screenshot of web-scanner report created using AI
Figure 10. Another screenshot of AI based web-scanner output


The existence of an automated AI-integrated web-scanner developed by an
underground threat actor highlights the continuous evolution and adaptability of
cybercriminals in keeping pace with modern times to stay ahead of the game. This
tool serves as a testament to the collaborative nature within the underground
community, where cybercriminals share ideas and learn from one another. The
custom web-scanner has already undergone improvements based on suggestions
received from the dark web community, resulting in the release of version 3 on
the Russian forum.

This flexible and customizable AI-based web-scanner exemplifies the potential
and knowledge that cybercriminals possess. It reflects their strong
determination to innovate and explore new frontiers in leveraging the
capabilities of GenAI. The tool's development and successive versions
demonstrate the dynamic nature of cyber threats and the relentless pursuit of
advancements by those with malicious intent.


CONCLUSION

As we delve into the complexities of GenAI and its implications for
cybersecurity, it becomes abundantly clear that this technological frontier is a
double-edged sword. While GenAI offers unparalleled opportunities for innovation
and efficiency, our examples in this blog show that misuse by cybercriminals
poses significant threats to global security and privacy. The adversarial use of
GenAI in advanced exploit development, deep fakes, custom tools and other
malicious activities underscores a stark reality: criminals are not bound by
ethics or regulations.

Reality brings us to a critical juncture where the responsibility of GenAI
developers and providers cannot be overstated. The call to action is clear and
urgent: on a global level, those at the forefront of GenAI innovation must
prioritize the implementation of robust security measures, ethical guidelines,
and oversight mechanisms. It is imperative that these stakeholders work
collaboratively with cybersecurity experts, policymakers, and international
organizations to establish a framework that not only fosters innovation but also
ensures the ethical use of GenAI technologies. The time to act is now as the
integrity of our digital world depends on it.

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