Preventing the misuse of AI models starts with architectural security measures like guardrails, data validation, prompt validation, and data loss prevention (DLP).
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Artificial intelligence (AI) systems are powerful, and many are embedded into essential business processes. Consequently, AI misuse can compromise applications and infrastructure, expose organizations to compliance and reputational risks, and in extreme cases even endanger lives. To prevent their misuse, AI models must have guardrails, access control, prompt validation, and other security measures in place. Architectural choices, such as incorporating human-in-the-loop (HITL) in AI-based application infrastructure, can also mitigate the risks of misuse.
AI misuse is the use of AI models for purposes other than the model architects' intended purposes, especially for malicious or fraudulent purposes. As AI models continue to become more effective, preventing AI misuse increases in importance. Many AI experts are concerned about AI's potential uses by rogue states and terrorists (parties that are likely already using AI to further their causes).
The OWASP Top 10 Risks for Large Language Models (LLMs) lists some of the ways AI models can be misused, such as prompt injection to manipulate their behavior, sensitive data disclosure, and introducing supply chain vulnerabilities by compromising an LLM that downstream applications rely on.
Beyond these risks, individuals might attempt to use AI models to access or generate dangerous or illegal content, from instructions for building a weapon to harmful explicit content.
For everyday users and businesses that rely on AI, preventing AI misuse is important for the sake of protecting their data, their brand, and their customers, as well as maintaining compliance with data privacy regulations.
Attackers can use AI models to aid in many types of cyber attacks. Generative AI models and AI agents can find software vulnerabilities, including, in some cases, zero day exploits. They can write malware programs. They can assist in social engineering campaigns by crafting phishing messages, and they may be able to identify phishing targets. Agentic AI applications could autonomously operate long-term phishing campaigns, ransomware campaigns, and other cyber attacks, empowering Advanced Persistent Threats (APTs) and organized criminal groups.
Even generative AI models with security guardrails in place can be misused in this way, thanks to techniques like prompt injection and jailbreaking that enable malicious parties to leverage the models for their own purposes.
To prevent individuals and groups from using AI applications for purposes other than their intended purpose, AI application and model developers should integrate a number of security measures throughout the development and deployment process.
Before a model is in production, it is trained. Preventing AI misuse starts with validating the training data to ensure a model's training data does not contain any biased data, any private data, or any hidden backdoors that allow for unexpected unauthorized behavior.
Because so much training data is needed to refine a model, it tends to come from a variety of sources, leaving training data vulnerable to supply chain attacks. But malicious parties might also use data poisoning attacks to corrupt training data, with the goal of introducing bias or backdoors on purpose. Data poisoners may also break directly into databases from outside the organization, or insider threats may corrupt training data.
Beyond data validation, these security measures help prevent data poisoning attacks:
Many organizations are not training LLMs themselves. For businesses that are downstream from LLM providers, it is important to understand what security measures they have taken to defend their models from data poisoning.
Customers of LLM providers typically use retrieval augmented generation (RAG) to optimize LLM performance for their use cases. Validating and securing the internal data sets used for RAG is essential as well.
AI guardrails are policies and controls that ensure AI models stay within predefined boundaries. Guardrails, for instance, can allow a model to write an email but stop it from writing a phishing email. Or, they can allow a model to code a function, but stop it from writing a vulnerability exploit.
Guardrails should defend AI models across all aspects, from training data (as described above) to application infrastructure.
Most organizations building AI into their public-facing applications are integrating preexisting AI models. Application and infrastructure guardrails, in these cases, are the areas in which they have the most direct control. They should also seek to understand the guardrails that the model providers have built into their models.
AI models are uniquely vulnerable to prompt injection attacks: deceptive prompts that trick a model into going outside of its guardrails. Aside from deliberate attacks, some user prompts might violate the model's Terms of Service, such as requests for illegal, dangerous, or explicit content.
Prompt validation helps ensure that prompts do not contain harmful or deceptive requests. Just as API schema validation blocks illegitimate requests that do not conform to the API's schema, prompt validation identifies and blocks unsafe content in prompts before they reach the AI model.
Human-in-the-loop (HITL) is one possible architectural approach to reduce the risks of unsupervised AI model decision-making. HITL keeps human managers part of the AI workflow so they can approve decisions made by AI models. Models can be trained with direct human feedback, or models may be configured to request human assistance when it can only make low-confidence predictions about the appropriate response to a prompt.
Data loss prevention (DLP) refers to a category of technologies that can stop confidential data from leaving secured environments. DLP can look at individual API requests and AI prompts, and using a multitude of techniques, including data fingerprinting, keyword matching, and pattern matching, DLP can identify sensitive and confidential data, and block requests where necessary.
DLP can also restrict copying and pasting from certain webpages or apps to prevent insiders from feeding internal information into external LLMs.
AI misuse can only be prevented if organizations have a complete view of where such misuse might be possible and might have an impact. AI models often end up embedded in application infrastructure in unexpected or unauthorized places, similar to the shadow API challenge faced by many app developers. Shadow AI detection helps organizations determine where the AI misuse risks are so that they can put appropriate guardrails and safety measures in place.
The Cloudflare AI Security Suite allows organizations to discover shadow AI, protect models from abuse, secure AI agent access, and block data exposure. This enables organizations to accelerate their rate of AI adoption while maintaining security. Learn more about the AI Security Suite.
AI misuse occurs when individuals or groups employ models for activities outside the models' original design, particularly for deceptive, illegal, or harmful goals. This includes using these tools to create dangerous or restricted content, or to facilitate fraudulent schemes.
Malicious parties can leverage generative AI to write malware, pinpoint software flaws, and discover zero-day exploits. They also use these tools to automate social engineering by generating convincing phishing messages and identifying potential targets for long-term spear phishing campaigns. Additionally, prompt injection attacks against generative AI models can allow attackers to discover confidential information.
Security begins during the training phase by validating data to ensure it is free from bias, private information, or hidden backdoors. AI model developers should also use diverse data sources, apply the principle of least privilege to data access, and utilize adversarial training to help the model recognize deceptive inputs.
Guardrails are essential policies and controls that keep AI behavior within safe, predefined limits.
Prompt validation acts as a filter that identifies and blocks deceptive or harmful requests before they reach the AI model. This process helps stop prompt injection attacks, where users try to trick the system into bypassing its safety measures.