Large Language Models (LLMs) process a wealth of sensitive information. They also introduce serious security challenges. Our previous article, Critical LLM Security Risks and Best Practices for Teams, examined core vulnerabilities such as prompt injection, data poisoning, and model theft. This article focuses on advanced best practices, and explores solutions to enhance security through adversarial testing, regular audits and monitoring, data encryption, and much more.
By implementing these strategies, your organization can mitigate emerging threats and develop resilient, reliable AI systems.
Why this matters
Malicious actors are becoming more advanced in their techniques. They now create complex, multi-layered attacks that take advantage of AI model vulnerabilities at different stages. Some known risks include:
- Prompt injection: Hackers manipulate instructions to extract or reveal private data.
- Data poisoning: Attackers plant corrupted or biased data that alters models or degrades performance.
- Mode theft: Unauthorized third parties try to replicate or steal a model’s unique capabilities.
LLMs can be misused for harmful purposes. Adversaries use designed prompts to test them, modify training data, and take advantage of insecure output management. This can threaten privacy, intellectual property, and the reliability of your applications–We will explore this more further down.
In addition, there are new areas of concern due to advanced attacks from malicious actors. These include:
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Jailbreaking: Jailbreaking involves methods that manipulate LLMs to produce restricted content by taking advantage of vulnerabilities in the model's security mechanism. This can lead to damage to a company's reputation and legal risks or may unintentionally result in harmful outputs.
In 2023, Chris Bakke took advantage of a Chevrolet dealership's ChatGPT-powered chatbot by convincing it to fulfill any customer request, allowing him to obtain a Chevy Tahoe worth seventy six thousand dollars for just one dollar. As news of the hack spread and others started to try it, the dealership had to shut down the chatbot.
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Hallucinations: Hallucinations occur when AI systems produce false or misleading information that seems accurate but is incorrect. For example, an AI chatbot that serves as an authoritative source for businesses could mislead businesses, creating legal risks.
One well-known incident is Air Canada's chatbot mistake, where a customer received wrong information regarding a bereavement fare. The airline didn’t honor this misinformation, and the British Columbia Civil Resolution Tribunal ruled against Air Canada, stating that they couldn't shift blame to the chatbot. This led to:
Damage to trust and reputation: Repeated error responses reduce user trust in AI, which harms brand credibility.
Legal and financial consequences*: Relying on AI for crucial decisions can expose businesses to legal risks or financial losses due to hallucination.
Model bias: Model bias occurs when large language models (LLMs) reflect the biases found in their training datasets. If these datasets contain historical, cultural, or systemic biases, the models can perpetuate and even intensify them, resulting in outputs that are discriminatory or unjust. A key impact is cultural and political bias, where LLMs trained on skewed datasets may support specific viewpoints while sidelining others.
API abuse: Malicious users can exploit application programming interfaces (API) to access sensitive information or misuse model functionalities, causing service interruptions. Potential consequences include:
Data extraction and model inversion: Attackers can create inputs that reveal proprietary training data, risking privacy and exposing sensitive business details.
Malicious prompt injection: By altering API queries, adversaries can bypass intended system controls, resulting in unauthorized actions.
Denial-of-service (DoS) attacks: Cybercriminals can inundate API endpoints with excessive requests, impairing performance and rendering the service unavailable.
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Uncontrolled content generation: Without adequate system checks, APIs may be exploited to produce misinformation, offensive material, or harmful code.
This article layers on more advanced measures that can be taken in combination with general recommendations.
Advanced security measures for LLMs
There are multiple known ways to protect LLMs from emerging threats, these measures include:
Role-based access control (RBAC)
Building on the need for access restrictions, apply a tiered system where each role has specific privileges. Operators can adjust parameters or redeploy models, while general users might only run inferences. This principle reduces an attacker’s blast radius because one compromised account won’t jeopardize your entire infrastructure.
Data encryption
Encryption must go beyond storage. Encrypt data both in transit and at rest. Keep your keys outside the direct environment of your LLM infrastructure. If an attacker breaks one barrier, they won’t access your plaintext data automatically. This helps prevent data leakage or infiltration of malicious training data.
Regular audits and monitoring
Set up logging that tracks changes, access patterns, and unusual behavior. Maintain logs for model input-output pairs to detect manipulations or anomalies. A robust auditing process flags suspicious usage spikes or repeated query patterns. Pair these logs with your security policies to halt suspicious activity quickly.
Prompt injection prevention
The preceding article outlined how attackers use trick prompts to bypass system safeguards. To upgrade your defenses, add automated filters that inspect user prompts before passing them to the model. Look for red flags, such as requests for hidden data or instructions to override system settings. Combine filtering with a quarantine workflow that halts suspicious prompts until an admin reviews them.
API rate limits and authentication
Rate-limiting is often overlooked, but it is key to preventing denial-of-service attacks. Throttling abnormal request bursts blocks spammers or malicious bots. Require strict authentication tokens for each call to your LLM endpoints. If your logs flag repeated failed authentication attempts, lock down the system until the threat is neutralized.
Adversarial testing
Conduct red-team exercises against your models. Feed them inputs that try to reveal hidden training data or override established rules. See how your LLM responds to suspicious behaviors. Then, refine your filters, regulations, and oversight to mitigate those discovered risks.
How to use KitOps and Jozu Hub to deepen your LLM security layer
The earlier piece discussed platform-based solutions like KitOps for packaging models and JozuHub for secure storage. However, this article goes further into how these tools provide advanced security.
KitOps is designed to improve your LLM security layer through a structured method for packaging models, tracking their progress, and verifying compliance. This approach include:
KitOps: ModelKit format and manifests
You can securely package your models using the ModelKit format, which keeps everything organized, including datasets, code, configurations, and documentation. The immutable manifest prevents unauthorized changes. If you modify anything, a new ModelKit is created, making every change traceable and transparent.
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SHA digests
When packaging your model, KitOps creates SHA digests for every file, serving as unique identifiers. If any part of your dataset or code is altered, the hash will change, indicating possible tampering.
Kitfile
The Kitfile is a YAML configuration file that gathers all model details, serving as your primary reference. It promotes consistency in team collaboration and eases the auditing process. Regularly reviewing and updating your Kitfile keeps your models well-documented and appropriately versioned.
While KitOps maintains a structured approach for packaging your models, Jozu Hub provides a mechanism for model attestation and tracking in a centralized platform. By integrating Jozu Hub into your AI security workflow, you can enforce best practices for governance, integrity, and verification.
Jozu Hub: Attestation and AI SBOM
Jozu Hub is a secure registry compliant with OCI 1.1, designed for storing and managing your ModelKits. By uploading your models here, you create a traceable version record that prevents unauthorized changes and simplifies retrieval.
Model attestation
Attestation confirms that a model hasn’t been altered without permission. The attestation process identifies the mismatch if a malicious party tries to switch out a file.
AI software bill of materials (AI SBOM)
Jozu Hub creates an AI Software Bill of Materials (AI SBOM) that details all dependencies in your model, including datasets, libraries, and code versions. This transparency helps you spot potential security risks and licensing problems early on.
Wrapping up
LLM security doesn’t stop at prompt injections or basic data encryption. It’s an evolving challenge that calls for constant vigilance. Keep strengthening your LLM setup. Combine the foundational security tips from our earlier published article with these advanced approaches. Guard your pipeline with multi-tiered access control, robust encryption, automated monitoring, strong API security, and thorough red-team testing.
Then, lean on tools like KitOps and Jozu Hub for packaging, tracking, and attestation. Their combined features give your teams a unified place to store, audit, and version everything from model weights to code files. This collaboration reduces friction between development and security. It also means you have a one-stop shop for investigating anomalies, verifying cryptographic authenticity, and confirming compliance obligations. Stay alert, stay informed, and use the right tools to keep your models safe from every angle.
Top comments (1)
Interesting piece!