Discover how Lakera's security solutions correspond with the OWASP Top 10 to protect Large Language Models, as we detail each vulnerability and Lakera's strategies to combat them.
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Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
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English to French Translation:
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
As organizations increasingly integrate Large Language Models (LLMs) into their operations, the Open Web Application Security Project (OWASP) Top 10 has become the go-to reference for understanding and mitigating the primary risks associated with these applications.
Before diving deeper into Lakera’s alignment with OWASP, let’s first better understand the OWASP framework.
The rise of LLMs has given birth to new use cases like LLM-based chatbots, RAG applications and intelligent document summarization tools.
By using LLMs, these applications are prone to novel cybersecurity threats that are specific to LLMs and have never been seen before.
The OWASP framework summarizes all the risks an LLM application faces, both LLM specific and traditional ones.
The framework comprises both threats occurring during development of the application as well as at deployment (“runtime”). The risks outlined in the framework are summarized below.
To better understand the OWASP framework, we will now exemplify the threats with the help of a customer service agent.
This customer service agent is deployed by a financial services company with the goal of assisting customers for various banking services.
The agent is based on an LLM and has been fine-tuned with historical customer service interactions.
Plus, it has access to an internal financial database containing sensitive information such as customer account details and transaction histories.
During runtime, such a customer service agent is prone to various forms of risks:
Meanwhile, vulnerabilities can also arise during the agent’s development and are outlined in the following:
The OWASP LLM Top 10 outline major threats to LLM applications, consisting of both novel risks like LLM01 Prompt Injection and traditional cybersecurity risks like LLM05 Supply Chain Vulnerability. Beyond the threats mentioned by OWASP, LLM applications are targets of further traditional cybersecurity threats such as credential theft or infrastructure attacks.
Holistically securing an LLM application requires addressing both traditional and LLM specific cybersecurity risks. This means complementing existing cybersecurity solutions with innovative, LLM-focused protections.
Lakera has been at the forefront of building novel AI cybersecurity solutions that complement the traditional security stack and can protect applications against LLM specific threats.
Below, we will outline how Lakera Guard, Lakera’s flagship product, protects LLM applications at runtime.
Lakera Guard protects LLM applications against LLM-specific risks at runtime. Acting as a firewall, Lakera Guard uses context-aware, state-of-the-art classifiers to detect prompt injections, data leakage and harmful content in both input and output of your LLM application.
Looking again at our imaginary example of the LLM-based customer service agent, Lakera Guard holistically secure against all runtime risks mentioned above by OWASP:
Through its prompt injection classifier, Lakera Guard detects and neutralizes any kind of prompt injections (LLM01 Prompt Injection). Besides, prompt injections and jailbreaks can induce further risks, as they are needed to gain excessive agency or trigger backdoors introduced by training data poisoning.
By neutralizing prompt injections, Lakera Guard helps to protect against these prompt injection-induced threats such as LLM03 Training Data Poisoning and LLM08 Excessive Agency. Lastly, as both inputs and outputs are being filtered, Lakera Guard also mitigates LLM02 Insecure Output Handling.
**💡 Pro Tip: Check out our Prompt Injection Attacks Handbook**
Similarly, Lakera Guard’s data leakage classifiers prevent spilling of sensitive data like PII, protecting LLM applications against LLM06 Sensitive Data/PII. This also blocks sharing of sensitive data with the LLM provider in case of PII input.
As part of Lakera Guard’s holistic protection, application outputs are also scanned for policy violations, harmful content, profanity and hate speech. This provides an additional layer of security needed for LLM09 Overreliance.
While Lakera Guard’s core focus lies on runtime protection, Lakera Guard’s API first approach allows deployment also in earlier stages of the software development lifecycle.
For instance, Lakera Guard can be used to analyze training data for prompt injections and jailbreaks, securing the application already during development against LLM03 Training Data Poisoning.
Reach out to us, and together we'll ensure your LLM applications are not only powerful and intelligent but also safe and trusted!
You can get started for free with Lakera Guard or book a demo here.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
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