AI vs AI: A Cybersecurity Threat
Imagine that after investing time and money, your company has finally set up a host of AI solutions. From LLMs to interact with customers, to AI agents to automate functions, and AI models that quickly compute and analyze large sets of proprietary data for you. You feel that your company is on a cusp of explosive growth.
Then, a cyberattack! But this attack isn’t from a human. It’s from another AI!
Yes, AI vs AI cyberattacks are a thing “ and it’s a growing threat.
The Adversarial AI
Introducing the ” adversarial AI “ what cybersecurity experts and researchers are calling cyberattacks that are executed by AI against AI systems.
The explosion of Large Language Models (LLMs) and generative AI tools has opened a particularly accessible and dangerous frontier. The adversarial AI uses the “Prompt Injection” , a cyberattack against the target AI that uses several different techniques.
In simple terms, the adversarial AI targets LLMs with text inputs, like a malicious text input, to override the LLM’s original instruction and any safety guardrails, or even extract sensitive information. The attack confuses the target AI’s understanding of its own directives and produces results that can cost and expose a business.
Why This Matters to Your Business Now
AI isn’t just in AI tools, it’s integrated in numerous third party tools that many SMBs contract. Tools like CMS, CRMs, DMP, even accounting software all have integrated AI into their platforms.
Therefore, whether you’re using AI for customer service, data analysis, cybersecurity, or automation, you are becoming a potential target for adversarial AI.
Understanding adversarial attack vectors will help you plan how to defend your company and how to best deploy AI tools and AI-enhanced third-party tools.
The adversarial AI uses several techniques to attack AI systems:
Trick the AI: in this attack, the adversarial AI might subtly alter an input (an image, a piece of text, an audio clip) just enough to trick a deployed AI model into making a wrong prediction or classification.
- Why does this work? Because unlike humans, AI models process data. For example, we as humans know what the color white vs yellow look like. But in HTML (for example), the color white is coded as #FFFFFF while yellow is coded #FFFF00. The adversarial AI would use a similar tactic to inject and trick the AI where the data is just subtly different.
- Real-World Concern: A facial recognition system fails to identify a known criminal because of subtly altered glasses. The resulting dangers can include direct operational failures, safety hazards, and bypassed security systems.
Poison the AI: in this attack, the adversarial AI “poisons” the target the AI’s training dataset by injecting corrupted, mislabeled, or malicious data into the training dataset. This compromises the model’s integrity and performance from the ground up, teaching it to make specific mistakes or ignore certain threats.
- Why does this work? Because unlike humans, AI models process data and like the previous example, the datasets with poisoned data can trick the target AI’s training. For example, if the target AI is shown a poisoned dataset that says “4+4=5”, that AI will think that four plus four equals five.
- Real-World Concern: Training an AI fraud detection system with poisoned data might teach it to ignore a specific pattern of financial fraud, allowing future fraudulent transactions to pass unnoticed. Accounting software like Quickbooks already employ AI-enhanced capabilities. Poisoning that software’s AI model on fraud or accounting discrepancy detection could allow accounting fraud to go unnoticed.
“Steal” from the AI: in this attack, the adversarial AI hits the target AI repeatedly through a series of queries to a proprietary “black box” AI model (where they can’t see its internal workings) and uses the outputs to reverse-engineer and reconstruct a copy of the model.
- Why does this work? With AI enhanced data processing, the adversarial AI keeps prompting the target AI with probes and takes notes of the responses. And with AI processing, can take those probe notes to reverse engineer and reconstruct a copy of the target AI model.
- Real-World Concern: Stealing a competitor’s valuable, custom-built AI algorithm, which represents years of research and development, without ever breaking into their servers. Loss of intellectual property, competitive disadvantage, and potential for further attacks using the stolen model.
Cybersecurity Solutions to Defend Against AI vs AI Attacks
The adversarial AI danger is just starting and growing. But understanding how they can attack your company now will position you to continuously defend against adversarial AI. How you defend against adversarial AI is by covering your cybersecurity basics:
- Implement best practices like multi-factor authentication (MFA)
- Strong passwords management
- Cyberattack training and drills to ensure you and your staff recognize potential attacks and take predefined actions.
A valuable investment is to consider consulting a vCIO, a virtual chief information officer, who can help give you a full 360 view of your cybersecurity. From connecting your IT systems to cybersecurity protocols, to reviewing your third-party vendor contracts to ensure that no technical backdoors are left with SaaS integration. The vCIO will be able to cover all of these bases for you and your company.
Whether you need cyberattack monitoring, securing devices, or vCIO services to help build out full IT and cybersecurity systems and protocols, Welltec Defense is ready to help your company.