Securing Generative AI and Large Language Models (LLMs): A Practical Guide

Discover best practices for securing Generative AI and Large Language Models (LLMs) in our comprehensive practical guide.
Securing Generative AI and Large Language Models (LLMs): A Practical Guide

Table of Content

Subscribe to latest Insights

By clicking "Subscribe", you are agreeing to the our Terms of Use and Privacy Policy.

The rapid advancements in Generative AI and Large Language Models have ushered in a new era where machines can read, understand, and generate human-like text. However, this transformative power brings a spectrum of security risks, challenges, and various potential vulnerabilities that demand our attention. From the subtleties of prompt injection to the complexities of insecure output handling or supply chain risks, the journey toward deploying secure and safe Generative AI applications is compelling and fraught with complexities.

In this article, we’ll explore a few key security concerns and potential vulnerabilities and provide actionable guidance to ensure the safe and secure deployment of Generative AI applications and LLMs.

Prompt Injections

Malicious prompts can manipulate LLM outputs, generating harmful content like hate speech or fake news or extracting sensitive information like passwords. It occurs when attackers use framed input prompts to manipulate a trusted large language model (LLM).

To overcome this vulnerability, identify untrusted input sources and implement NLP-based robust validation and sanitization methods to identify suspicious patterns and filter out potential malicious prompt inputs.

Insecure Output Handling

Unfiltered LLM outputs can expose biases, discriminatory content, or privacy-sensitive details, such as unmoderated comments on social media platforms. Blindly trusted LLM output can result in XSS or CSRF in web browsers, as well as privilege escalation, SSRF, or remote code execution on backend systems, and impact essentially increases if LLM output is used as-is to perform any next action.

Establish robust content moderation mechanisms to review and filter offensive language, harmful content, and sensitive information in LLM outputs. Ensure access controls, authorizations, and encode output to mitigate undesired JavaScript or Markdown code interpretations.

Data Poisoning

A malicious user may inject toxic, inaccurate, or malicious documents into the model’s training data, and the victim model trains using falsified information, which leads to an incorrect, harmful, and biased response. Another case could be when a large language model is trained on unverified training data that negatively influences the application output.

Perform regular audits to verify the authenticity of training data (especially if sources are external). Implement red teaming exercises and vulnerability scanning into the model training and testing pipelines of the LLM’s lifecycle. Evaluate LLM performance and benchmarking rigorously using metrics such as BLEU and ROUGE.

Denial of Services

An attacker may repeatedly send highly resource-consuming requests to your Generative AI application, causing outages for legitimate users and increased resource bills. Malicious users could exploit vulnerabilities in task queues by submitting a large number of computationally expensive tasks that increase the backlogs, slow down requests, and eventually complete system failures.

To mitigate this risk, implement stringent rate-limiting for requests per user, the complexity of prompts, request time-outs, and cap computational resources per query. Additionally, deploy access controls, a captcha, and multi-factor authentication if your LLM exposes sensitive information. Continuous monitoring can help detect anomalies, suspicious patterns, and repetitive resource-intensive requests.


Due to the potential benefits, our reliance on Gen AI applications is increasing rapidly. Excessive dependency on LLMs from content generation to decision-making has become a super security vulnerability. LLM’s creative capability to generate content could also produce nonsensical, inappropriate, or hallucinations. For example, an LLM may inaccurately describe historical events or generate a grammatically correct story that doesn’t make logical sense or any misleading content, causing potential negative consequences.

We must read, review, and fact-check the output generated by LLMs to ensure they are factual, coherent, and appropriate. Verify the source and accuracy of information provided by LLMs before using it for any critical decision-making. It’s crucial to understand that LLMs should considered as collaborative tools, offering valuable guidance rather than entirely replacing human judgment. 

As we embrace the capabilities of Generative AI and LLMs, ensuring their vulnerabilities, risks, and safety is a shared responsibility. By adopting proactive measures, regular inspections, staying informed about potential business risks, and fostering a security mindset, we can harness the full potential of this magical technology.

Join us on this exploration as we navigate the delicate balance between innovation and safeguarding, ensuring that the promises of Generative AI and LLMs unfold securely, responsibly, and resiliently.