AI has enabled hackers to discover new ways to automate attacks using ML algorithms and Natural Language Processing (NLP). This new wave of AI-powered hacking is revolutionizing how hackers operate, taking cybersecurity to a new level. Due to this, hackers can locate vulnerabilities faster, making them a real threat to firms.
In a strange twist of technological fate, one of the smartest tools in the hands of enterprises could perhaps be their worst enemy as well. AI, has two distinct sides to its utility- fight crime and also create almost invincible cyber-attacks.
Mario Duarte, Former VP of Security at Snowflake, says,
The Rise of the ChatGPT’s Evil Cousins
Generative pre-trained transformers (GPT) architecture has shown immense potential- from NLP to content creation. However, it has found a malicious application in the hands of hackers, giving rise to WormGPT, Evil-GPT, XXXGPT, Wolf GPT, and Fraud GPT.
These black-hat AI tools use ChatGPT to
- craft sophisticated phishing emails
- deceive users with legitimate messages
- yield highly personalized social engineering attacks.
These black hat AI tools use AI-generated content to exploit human psychology and breach digital defenses.
Duarte further adds,
1. FraudGPT
FraudGPT can help create realistic phishing texts and emails to phish victims of all their sensitive data. Hackers can also use it as a spear-phishing tool to create emails, luring specific users to give away sensitive information. At the same time, attackers who aren’t native English speakers can use this tool to write emails in English.
2. WormGPT
Like other black hat AI tools, WormGPT has no ethical boundaries and helps orchestrate many illegal activities. It is trained on data related to malware creation. The tool can fabricate Python malware, guiding the crafting malicious attacks. It also helps hackers design highly convincing Business Email Compromise (BEC) emails.
Max Heinemeyer, Chief Product Officer at Darktrace, says,
“2023 has been the year attackers test things like WormGPT and FraudGPT and adopt AI in their attack methodologies. 2024 will show how more advanced actors like APTs, nation-state attackers, and advanced ransomware gangs have started to adopt AI.
“The effect will be even faster, more scalable, more personalized & contextualized attacks with a reduced dwell time.”
“It could also be the year of attackers combining traditional worming ransomware – like WannaCry or notPetya – with more advanced, AI-driven automation to create an aggressive autonomous agent with sophisticated, context-based decision-making capabilities.”
3. XXXGPT
XXXGPT uses generative models to generate malicious code. It uses an LLM to generate malware from its training sets and create convincing malware that is difficult to detect. The tool also has an “obfuscation” feature that helps mask the code generated from the model. This makes it even harder for prevention and detection.
4. Wolf GPT
Wolf GPT also uses generative models to generate malicious code. It provides anonymity to the attacker within specific attack vectors. This AI system can generate realistic malware using large datasets of existing malicious software. Like XXXGPT, Wolf GPT also has an obfuscation feature, making it hard for firms to detect and block incoming threats.
How Can Firms Manage the AI-based Hacking Risks in 2024?
1. Data Breaches
AI systems rely on data, often having sensitive information. Without tight security measures, there is always an increased risk of data breaches and unauthorized access.
Firms must ensure that data protection measures, like encryption, access controls, and secure data storage, are implemented. Conduct regular security assessments and audits to identify vulnerabilities in AI systems to minimize the risk of data breaches.
2. Adversarial Attacks
AI models are liable to adversarial attacks, where hackers exploit vulnerabilities to manipulate the model’s behavior. These attacks compromise the decision-making processes, leading to financial losses or reputational damage.
Firms must collaborate closely with data scientists to implement practices such as robust model training and anomaly detection. They must encourage using adversarial examples to strengthen AI models against attacks.
Strict access controls and continuous monitoring can also help detect and mitigate unauthorized model manipulation endeavors.
Nicole Carignan, VP of Strategic Cyber AI at Darktrace, adds,
“Adversaries will focus their efforts on improving and optimizing autonomous agents (e.g., AutoGPT) to augment on-demand attacks. As autonomous agents get more sophisticated, they can pivot better and improve their decision-making about the best next step to advance an attack. ”
“Currently, autonomous agents have limited capacity to make complex decisions, but as adversaries focus on optimizing and training existing agents, they will become even more capable of targeted and sophisticated actions. This will be a particular focus for nation-state adversaries.”
3. Compliance, Ethical Implications, and Bias
The use of AI in business processes raises compliance challenges. Hence, ensuring that AI models and data processing activities comply with laws, regulations, and industry standards is essential. Failure to meet these requirements may result in fines, legal action, or reputational damage.
Moreover, training data with biases can inadvertently be perpetuated by AI systems. This results in social and ethical implications and legal consequences.
Firms must work closely with legal and compliance teams to set comprehensive governance frameworks. They must conduct privacy impact assessments and implement “data subject rights management” mechanisms to mitigate legal and regulatory risks. Furthermore, the training data must be rigorously evaluated to identify and mitigate biases.
To address ethical concerns and prevent biases-
- Implement data governance frameworks
- Involve stakeholders in the development process
- Audit AI systems regularly
4. Insider Threats and Privileged Access Misuse
As per a research by Ponemon, “2022 Cost Of Insider Threats Global Report,”
- 56% of incidents experienced by organizations represented in this research were due to negligence
- The average annual cost to remediate the incident was USD 6.6 million
- It took an average of 85 days to contain the incident, an increase from 77 days in the previous study
- Only 12% of incidents were contained in less than 30 days
- A total of 3,807 attacks, or 56%, were caused by employee or contractor negligence, costing on average USD 484,931 per incident
- Malicious insiders caused 26% or 1,749 incidents at an average cost per incident of USD 648,062
Integrating AI can create new routes for insider threats. Employees with privileged access may misuse their privileges or manipulate algorithms for malicious intent.
Therefore, firms must enforce strong access controls. Also, conduct regular security awareness training and implement behavioral analytics to detect suspicious activities. They can minimize the risk of insider threats by monitoring user behavior and implementing the principle of least privilege.
Also Read: Protecting Enterprises from Black Hat Hackers
Conclusion
While organizations adopt AI, they must address specific security risks alongside its numerous benefits. In 2024, they must responsibly mitigate these risks with robust protection measures against data breaches and adversarial attacks.
Moreover, by ensuring legal and regulatory compliance and mitigating insider threats, firms can securely embrace the potential of AI. In the future, dynamic security measures and a proactive approach to risk management will help firms confidently integrate AI into their business processes while safeguarding their customers’ interests and trust.
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