by Bruce Snell, Cybersecurity Strategist, Qwiet AI
The rapid integration of Generative AI and Large Language Models (LLMs) is revolutionizing the technological landscape, bringing innovation to the forefront. These technologies are proving to be powerful tools, enabling breakthroughs in content generation, data analysis, and automation. However, the surge in the use of generative AI and LLMs brings a pressing need to address new security concerns associated with their adoption.
The Rush to Embrace Generative AI and LLMs
Generative AI and LLMs have rapidly gained popularity across industries, from healthcare and finance to manufacturing, with businesses eager to harness the benefits and advantages these technologies offer.
These generative tools provide the ability to create content: text, code, and even multimedia content. Their use in data analysis allows for more efficient and insightful examination of vast datasets, thereby increasing productivity. Additionally, automation capabilities empower organizations to streamline tasks and enhance user experiences. But these enhancements can come at a cost. Cybersecurity is always an arms race, and AI can be a tool for both good and bad, with real risks for industries.
Emerging Security Concerns
The integration of generative AI and LLMs has introduced a new set of security challenges and vulnerabilities that developers must confront. This summer, the Open Worldwide Application Security Project (OWASP) released a list of the top 10 most critical vulnerabilities often seen in generative technology, highlighting the potential impact and ease of exploitation. While not all the vulnerabilities afford the same level of risk, understanding and addressing these issues is crucial to protect against attacks. This includes:
- Prompt Injection: The ability to manipulate the inputs or prompts given to generative AI and LLMs can lead to malicious outcomes. Unintended results, misinformation, or harmful content may be generated when these systems receive malicious instructions.
- Data Poisoning: Attackers may attempt to compromise the training data used by these models, injecting biased or malicious information. This could result in the generation of biased content or even manipulative narratives.
- Sensitive Information Disclosure: There is a risk of unintentional data leakage when using generative AI and LLMs. Users must be cautious about sharing sensitive data that these models could inadvertently reveal in generated content.
- Overreliance: Overreliance on generative AI and LLMs can lead to a lack of human oversight and accountability. Blindly trusting AI-generated content without verification can have far-reaching consequences, especially when dealing with critical information or decision-making processes.
AI and LLMs can also be used to strengthen more traditional cyberattacks, including social engineering. Phishing is an area that could easily benefit from AI. Typically, phishing emails are rife with misspellings and grammatical errors, but a quick run through a generative AI tool can allow cybercriminals to craft more realistic emails and messages, making them much more likely to be clicked on by an unsuspecting victim.
Finding a Balance
To strike a balance between innovation and security, it is crucial to take a proactive approach and ensure the responsible use of AI and LLMs. This includes implementing robust security practices to safeguard against prompt injection, data poisoning, and data leakage. Companies must also ensure these systems only generate content that aligns with ethical and legal guidelines and take stringent measures to protect privacy and prevent unintended disclosures.
AI can also help teams improve their defenses again attacks. If you wait for every new variant of an attack to come out and THEN create a defense for it, you’ll always be behind the curve. AI can be used to create adaptive and flexible rules that can protect against whole classes of attacks, catching any variants the bad guys use to get around more traditional security technologies. By harmonizing AI’s predictive capabilities with AppSec, like we are doing at QWIET AI, we’re transitioning from merely countering threats to preemptively identifying and mitigating them.
For developers specifically, AI-based AppSec tools can help resolve AI-generated problems AND minimize disruption. AppSec teams are overwhelmed by false positives when testing their code. Employing AI to identify actual exploits that pose a threat to an organization allows developers to reclaim time and budget that might otherwise be spent pursuing potential vulnerabilities, while maintaining protection against attacks.
The integration of generative AI and Large Language Models represents a significant leap in technological innovation. However, the rush to embrace these tools must be tempered with a conscious effort to address emerging security concerns. It is essential for all stakeholders, from developers and organizations to policymakers and consumers, to recognize the broader implications and challenges associated with this innovative technology. By doing so, we can harness the power of generative AI and LLMs while safeguarding our digital landscape from potential risks and threats.
About the Author
Bruce Snell is the Cybersecurity Strategist for Qwiet AI. He has over 25 years in the information security industry. His background includes administration, deployment, and consulting on all aspects of traditional IT security. For the past 10 years, Bruce has branched out into OT/IoT cybersecurity (with GICSP certification), working on projects including automotive pen-testing, oil and gas pipelines, autonomous vehicle data, medical IoT, smart cities, and others. Bruce has also been a regular speaker at cybersecurity and IoT conferences as well as a guest lecturer at Wharton and Harvard Business School, and co-host of the award-winning podcast “Hackable?”. Bruce can be reached online on X and LinkedIn and at https://qwiet.ai/
