Question: What do we really know about Large Language Model (LLM) security? And are we willingly opening the gateway to chaos by using LLMs in business?
Rob Gurzeev, CEO of CyCognito: Picture it: your engineering team is leveraging the immense capabilities of LLMs to “write code” and quickly develop an application. It’s a game changer for your businesses; development speeds are now orders of magnitude faster. You reduced your time to market by 30%. It benefits your organization, your stakeholders and your end users.
Six months later, your request is reported to be leaking customer data; it was jailbroken and its code was manipulated. You are now when faced with SEC violations and the threat of customers leaving.
The efficiency gains are attractive, but the risks cannot be ignored. While we have well-established standards for security in traditional software development, LLMs are black boxes that require us to rethink how we ensure security.
New types of security risks for LLMs
LLMs are fraught with unknown risks and subject to attacks never before seen in traditional software development.
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Timely injection attacks they involve manipulating the model to generate unintended or harmful responses. Here, the attacker strategically makes suggestions to cheat the LLM, potentially bypassing security measures or ethical constraints put in place to ensure responsible use of artificial intelligence (AI). As a result, LLM responses may significantly deviate from intended or expected behavior, posing serious risks to the privacy, security, and reliability of AI-based applications.
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Insecure output handling occurs when the output generated by an LLM or similar AI system is accepted and incorporated into a software application or web service without undergoing adequate vetting or validation. This can expose back-end systems to vulnerabilitiessuch as cross-site scripting (XSS), cross-site request forgery (CSRF), server-side request forgery (SSRF), privilege escalation, and remote code execution (RCE).
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Training data poisoning occurs when the data used to train an LLM is deliberately manipulated or contaminated with harmful or biased information. The process of training data-poisoning typically involves inserting deceptive, misleading, or malicious data points into the training dataset. These instances of manipulated data are strategically chosen to exploit vulnerabilities in the model’s learning algorithms or to instill biases that could lead to undesirable outcomes in the model’s predictions and responses.
A blueprint for securing and controlling LLM applications
While some of this is new territoryThere are best practices you can implement to limit exposure.
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Sanitization of entrances involves, as the name suggests, the Sanitization of inputs to prevent unauthorized actions and data requests initiated by malicious instructions. The first step is input validation to ensure that the input adheres to expected formats and data types. Next is input sanitization, where potentially malicious characters or code are removed or encoded to thwart attacks. Other tactics include whitelists of approved content, blacklists of prohibited content, parameterized queries for database interactions, content security policies, regular expressions, continuous logging and monitoring, and security updates and testing.
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Output control AND rigorous management and evaluation of the output generated by the LLM to mitigate vulnerabilities, such as XSS, CSRF and RCE. The process begins by validating and filtering LLM responses before accepting them for submission or further processing. It incorporates techniques such as content validation, output tagging, and output escaping, all of which aim to identify and neutralize potential security risks in the generated content.
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Safeguarding training data it is essential to prevent training data poisoning. This involves applying strict access controls, using encryption to protect data, maintaining data backups and version control, implementing data validation and anonymization, creating records and comprehensive monitoring, conducting regular audits and training employees on data security. It is also important to verify the reliability of data sources and ensure secure storage and transmission practices.
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Enforcement of strict sandboxing policies and access controls it can also help mitigate the risk of SSRF exploits in LLM operations. Techniques that can be applied here include sandbox isolation, access controls, whitelisting and/or blacklisting, request validation, network segmentation, content type validation, and content inspection . Regular updates, thorough employee registration and training are also key.
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Continuous monitoring and content filtering can be integrated into the LLM processing pipeline to detect and prevent harmful or inappropriate content, using keyword-based filters, contextual analysis, machine learning models and customizable filters. Ethical guidelines and human moderation play a key role in maintaining responsible content generation, while continuous real-time monitoring, user feedback loops and transparency ensure that any deviations from desired behavior are promptly addressed.