59 lines
7.7 KiB
Markdown
59 lines
7.7 KiB
Markdown
- LLM security risks encompass various vulnerabilities that can compromise the integrity, confidentiality, and availability of large language models. These risks include **prompt injection, data poisoning, sensitive information disclosure**, and more. Prompt injection allows malicious actors to manipulate the model's behavior by inserting malicious input, while data poisoning involves contaminating the training data to negatively impact the model's accuracy. Sensitive information disclosure can lead to the exposure of confidential data, and insecure output handling can create vulnerabilities in downstream systems. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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Here's a more detailed breakdown of some key LLM security risks: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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1. Prompt Injection: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- Attackers can craft specific inputs (prompts) to manipulate the LLM's behavior, potentially leading to the generation of harmful content, disclosure of sensitive data, or other unauthorized actions. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- This is a significant concern because LLMs rely on user input to generate text, making them susceptible to manipulation. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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2. Training Data Poisoning: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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- Malicious actors can intentionally introduce biased or misleading information into the LLM's training dataset, which can then negatively impact the model's performance and accuracy.
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- This can lead to the model generating incorrect or biased outputs, or even being used for malicious purposes. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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3. Sensitive Information Disclosure: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- LLMs trained on vast datasets may inadvertently reveal sensitive information from their training data or user inputs. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- This can include personal data, confidential business information, or other sensitive data that should not be publicly disclosed. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [3](https://www.exabeam.com/explainers/ai-cyber-security/llm-security-top-10-risks-and-7-security-best-practices/)]
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4. Insecure Output Handling: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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- If the LLM's outputs are not properly validated or sanitized before being used in a downstream application, it can create vulnerabilities that can be exploited by attackers. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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- This can lead to various security risks, such as injection attacks or data leaks. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks), [4](https://kili-technology.com/large-language-models-llms/research-and-methods-on-ensuring-llm-safety-and-ai-safety#:~:text=Insecure%20Output%20Handling:%20Insecure%20output%20handling%20occurs,leaks%2C%20or%20the%20propagation%20of%20harmful%20content.)]
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5. Model Denial of Service: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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- Attackers can exploit the LLM's resources to degrade its performance or cause it to become unavailable, effectively disrupting its functionality.
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- This can lead to outages, increased operational costs, and a loss of service for users. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [2](https://www.tonic.ai/guides/llm-security-risks)]
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6. Supply Chain Vulnerabilities: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [5](https://www.lasso.security/blog/llm-security)]
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- LLMs and the applications that interact with them often rely on third-party components and services, which can introduce vulnerabilities into the system.
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- Compromised third-party libraries or services can be exploited to gain access to the LLM or the underlying infrastructure. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/), [5](https://www.lasso.security/blog/llm-security)]
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7. Excessive Agency: [[5](https://www.lasso.security/blog/llm-security)]
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- If LLMs or their associated agents and plugins are given too much power or access, they can be manipulated to perform unwanted actions.
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- This can lead to unauthorized access, data breaches, or other security compromises. [[5](https://www.lasso.security/blog/llm-security)]
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8. Model Theft: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- Malicious actors may attempt to copy, exfiltrate, or steal the proprietary LLM models, which can lead to economic losses, reputational damage, and unauthorized access to sensitive data.
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- This is a serious concern for organizations that have invested heavily in training and developing LLMs. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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9. Overreliance: [[5](https://www.lasso.security/blog/llm-security)]
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- Organizations that rely too heavily on LLMs without proper validation or oversight can be vulnerable to various risks, including errors, misjudgments, and security lapses.
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- It's crucial to understand the limitations of LLMs and to implement appropriate safety measures to mitigate these risks. [[5](https://www.lasso.security/blog/llm-security)]
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Mitigating LLM Security Risks: [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- Implementing robust security measures, such as prompt sanitization, data validation, and access controls. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- Using secure execution environments, such as sandboxes, to isolate the LLM from other systems. [[1](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)]
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- Regularly updating LLMs and their associated software and libraries to patch vulnerabilities. [[2](https://www.tonic.ai/guides/llm-security-risks)]
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- Developing and enforcing policies for the responsible use of LLMs, including data privacy and intellectual property rights. [[2](https://www.tonic.ai/guides/llm-security-risks)]
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*Generative AI is experimental.*
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[1] [https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/](https://www.cloudflare.com/learning/ai/owasp-top-10-risks-for-llms/)
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[2] [https://www.tonic.ai/guides/llm-security-risks](https://www.tonic.ai/guides/llm-security-risks)
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[3] [https://www.exabeam.com/explainers/ai-cyber-security/llm-security-top-10-risks-and-7-security-best-practices/](https://www.exabeam.com/explainers/ai-cyber-security/llm-security-top-10-risks-and-7-security-best-practices/)
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[4] [https://kili-technology.com/large-language-models-llms/research-and-methods-on-ensuring-llm-safety-and-ai-safety](https://kili-technology.com/large-language-models-llms/research-and-methods-on-ensuring-llm-safety-and-ai-safety#:~:text=Insecure%20Output%20Handling:%20Insecure%20output%20handling%20occurs,leaks%2C%20or%20the%20propagation%20of%20harmful%20content.)
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[5] [https://www.lasso.security/blog/llm-security](https://www.lasso.security/blog/llm-security)
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