Growing adoption of large language models (LLMs) means expanded attack surfaces, which means increased risk, which means a system breach is more likely a “when” than an “if.” And that means security for the AI and other digital systems in an organization must be addressed. Now.
The consequences of a security breach via an LLM are substantial, ranging from loss of sensitive data and eroded stakeholder trust to significant financial and legal repercussions. In this digital world we occupy, addressing LLM vulnerabilities is essential for technical security and for maintaining operational integrity and financial health. This blog post explores how CalypsoAI’s wraparound LLM security and enablement solution addresses the 10 LLM application security concerns according to the OWASP standards.
LLM01 – Prompt Injections
- The Threat: Prompt injections, or “jailbreaks,” occur when threat actors use manipulative language structures to exploit a model. When successful, these attacks bypass control mechanisms and direct the model to act outside its intended parameters, thereby compromising its integrity and reliability.
- Mitigation: CalypsoAI scanners review every prompt for numerous manipulative language structures and sentiment polarity, blocking or redacting those that meet or exceed organizationally-selected thresholds. This robust input validation and monitoring substantially reduces prompt injection risks.
LLM02 – Insecure Output Handling
- The Threat: Too often, users accept model-generated responses without adequate scrutiny, which can expose back-end systems to cyber threats such as cross-site scripting (XSS), cross-site request forgery (CSRF), and remote code execution.
- Mitigation: CalypsoAI’s platform provides users the broadest suite of customizable content scanners in the industry, as well as the ability to create bespoke scanners to fit their business needs. Every response is rigorously scanned to identify potentially harmful content, with malicious or suspicious content blocked or sanitized. The option to require real-time human verification adds an extra layer of security, ensuring even the most subtle anomalies are detected by in-house subject matter experts.
LLM03 – Training Data Poisoning
- The Threat: Training data can be manipulated, or “poisoned,” when vulnerabilities, biases, or other exploitable data are introduced and thus compromise the LLM’s security, effectiveness, and/or ethical responsiveness, leading to flawed decisions.
- Mitigation: CalypsoAI integrates layers of both automated and human review and validation to ensure the accuracy and reliability of LLM responses. This dual-layer review process is critical for identifying and rectifying biases or anomalies that solely-automated systems might miss.
LLM04 – Model Denial of Service (DoS)
- The Threat: Model DoS attacks can degrade service quality and significantly increase operational costs by initiating resource-heavy operations.
- Mitigation: CalypsoAI’s platform offers enhanced auditability features and strategic resource allocation to manage model loads effectively. Policy-based access controls allow administrators to implement rate limits for each model to maintain performance and reliability.
LLM05 – Supply Chain Vulnerabilities
- The Threat: Integrating potentially vulnerable components or services can introduce significant security risks, compromising the entire application lifecycle.
- Mitigation: CalypsoAI allows organizations to customize LLM usage and construct a reliable information taxonomy, and prevents unauthorized data importation or usage via stringent content and access controls.
LLM06 – Sensitive Information Disclosure
- The Threat: Inadvertent inclusion of private or confidential information in prompts can lead to data breaches and compliance violations.
- Mitigation: CalypsoAI’s advanced, customizable scanners review prompts and responses for personally identifiable information (PII), data loss prevention (DLP), intellectual property (IP), secrets, source code, and other sensitive data to prevent the disclosure of confidential or proprietary information. Identified content is blocked or redacted, according to administrator-set thresholds and parameters.
LLM07 – Insecure Plugin Design
- The Threat: Insecure plugins can lead to severe consequences, such as remote code execution, due to inadequate access control mechanisms.
- Mitigation: CalypsoAI enables administrators to employ strict controls to ensure plugin access is limited to authorized users and to scan plugin responses for malicious content. The suite of recognized computer languages in the scanners is continually updated, enabling malicious or foreign code to be detected in real time.
LLM08 – Excessive Agency
- The Threat: Excessive functionality, autonomy, or access granted to LLMs can lead to unintended consequences and security breaches when the models operate beyond their intended parameters.
- Mitigation: Implementing CalypsoAI’s granular policy-based access controls and model-specific rate limits enables fast, easy management of users’ interactions with LLMs, preventing misuse and abuse. Real-time monitoring and auditing features allow administrators to glean detailed insights about those interactions, which enables analysts to identify usage patterns and anomalies, assess system effectiveness, and ensure compliance with organizational rules and policies, such as Acceptable Use. Analytics about actual use provides organizations the information they need to fine-tune their models, resulting in stronger security, better user experiences, and improved decision-making.
LLM09 – Overreliance
- The Threat: Overreliance on LLMs without appropriate oversight can lead to the creation of content that provokes organizational challenges, as well as ethical, legal, and/or compliance issues.
- Mitigation: CalypsoAI’s platform includes continually updated syntax-based source code and malware scanners and customizable, bi-directional filters for toxicity, bias, and banned content, as well as optional, administrator-written disclosure agreements to enforce responsible LLM usage. Optional human-in-the-loop review and validation can further ensure the accuracy and appropriateness of model-generated content.
LLM10 – Model Theft
- The Threat: Unauthorized access to or adversarial attacks on proprietary models, which are an organization’s IP, can lead to exfiltration, copying, and outright co-opting or theft of the models. In addition to the property and privacy losses, such activity can create significant economic and competitive disadvantages.
- Mitigation: CalypsoAI’s platform enables real-time content and behavioral monitoring, with automated blocking/redaction, as well as model-specific rate limits based on administrator-identified parameters to protect against model theft.
Addressing these LLM-targeted vulnerabilities enables organizations to safeguard their LLM applications, ensuring secure and effective deployment for daily company operations. CalypsoAI’s security and enablement trust layer provides a comprehensive approach to mitigating risks, maintaining the integrity of LLMs, and ensuring your GenAI integrations are robust, secure, and resilient. To learn more about how our novel solution can safeguard your LLM applications and allow your organization to stay ahead of emerging threats, download our ebook OWASP Top 10 for LLMs: Protecting Large Language Models with CalypsoAI.
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