OWASP Top 10 Explained for Large Language Model Application Security

OWASP Top 10 Explained for Large Language Model Application Security

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GPT's introduction caused a stir, extending AI access beyond tech enthusiasts. Despite its linguistic prowess and practical applications, security concerns such as malware and data breaches remain significant. Organizations need to weigh these benefits against potential security risks carefully.

It is essential to take practical measures and prepare for current and future security challenges to safely leverage large language models (LLMs) like ChatGPT.

Addressing AI model security, the OWASP Foundation has released an extensive list detailing the top 10 security risks associated with LLMs.

What are GPT and LLM?

GPT, or Generative Pre-trained Transformer, is an NLP model created by OpenAI, designed to understand and generate human-like text based on a given input. GPT-4, the latest version, is the most significant and renowned in the GPT series.

LLMs, or Large Language Models, are a broader category that includes various language models similar to GPT. While GPT models are a subset of LLMs, the term “LLM” collectively refers to any large-scale language model specialized in natural language processing tasks.

LLMs function as sophisticated next-word prediction engines. Notable examples of LLMs besides OpenAI’s GPT-3 and GPT-4 include Google's LaMDA and PaLM (the foundation for Bard), Hugging Face’s BLOOM and XLM-RoBERTa, and Nvidia’s NeMO LLM, XLNet, and GLM-130B.

OWASP Top 10 Security Risks for LLMs

Originally focused on identifying the top 10 web application vulnerabilities, OWASP has expanded its scope to include various vulnerability lists, such as those for APIs, mobile applications, and LLMs.

The OWASP LLM Top 10 outlines key vulnerabilities associated with LLMs. Released to inform development teams and organizations, it emphasizes understanding and mitigating potential risks in adopting large language models. The primary vulnerability in the LLM category is Prompt Injection. Research has uncovered six new vulnerabilities: Training Data Poisoning (LLM03), Supply Chain Vulnerabilities (LLM05), Insecure Plugin Design (LLM07), Excessive Agency (LLM08), Overreliance (LLM09), and Model Theft (LLM10).

Additionally, four vulnerabilities align with existing OWASP API top 10 vulnerabilities: Prompt Injection (LLM01), Insecure Output Handling (LLM02), Model Denial of Service (LLM04), and Sensitive Information Disclosure (LLM06).

Thus, the OWASP Top 10 for LLMs are as follows:

  • LLM01: Prompt Injection – Manipulation of input prompts to compromise model outputs and behavior.

  • LLM02: Insecure Output Handling – Flaws in managing and safeguarding generated content, risking unintended consequences.

  • LLM03: Training Data Poisoning – Introducing malicious data during model training to manipulate its behavior.

  • LLM04: Denial of Service – Deliberate actions causing the model to become unresponsive or unusable.

  • LLM05: Supply Chain – Vulnerabilities arising from compromised model development and deployment elements.

  • LLM06: Sensitive Information Disclosure – Unintended disclosure or exposure of sensitive information during model operation.

  • LLM07: Insecure Plugins Designs– Vulnerabilities in external extensions or plugins integrated with the model pose security threats.

  • LLM08: Excessive Agency – Overly permissive model behaviors that may lead to undesired outcomes.

  • LLM09: Overreliance – Overdependence on model capabilities without proper validation, leading to potential issues.

  • LLM10: Model Theft – Model theft in LLMs poses a risk when attackers gain access to the creators’ network through employee leaks or intentional/unintentional extraction attacks.

1. Prompt Injections: Manipulating the Language Model

Prompt injections present significant security threats to Language Model (LM) applications by exploiting weaknesses in the model's input processing and comprehension. These vulnerabilities can lead to serious issues, including data leaks, unauthorized access, and other breaches. Understanding and addressing these risks is crucial to maintaining the security and integrity of LM applications.

Let's examine some common vulnerabilities caused by prompt injection.

Common Prompt Injection Vulnerabilities

LLM applications can suffer from various data leakage vulnerabilities through prompt injections. These include:

  • Manipulating the LLM: Crafted prompts can trick the LLM into revealing sensitive information, bypassing content filters or restrictions, or gaining unauthorized access by using specific language patterns or tokens. Attackers exploit weaknesses in tokenization or encoding to extract confidential data or gain unauthorized access.

  • Contextual Manipulation: Providing a misleading context within prompts can cause the LLM to perform unintended actions. Attackers may deceive the model into generating inaccurate or harmful outputs, influencing user or system behavior in undesirable ways.

How to Prevent Prompt Injections

To enhance the security of LLM applications and mitigate prompt injection risks, implement the following measures:

  1. LLM Application Security Testing: Conduct thorough security testing, including assessments for prompt injection vulnerabilities, to identify and address potential weaknesses before deployment.

  2. Strict Input Validation and Sanitization: Apply robust input validation and sanitization processes to check user-provided prompts. This helps prevent unauthorized or malicious prompts from being accepted.

  3. Context-Aware Filtering: Use context-aware filtering mechanisms to detect and prevent prompt manipulation. Analyze prompt context against predefined rules to identify suspicious patterns and block prompt injections, even when specific language patterns, tokens, or encoding mechanisms are used.

  4. Regular Updates and Fine-Tuning: Continuously update and fine-tune the LLM application to improve its understanding of malicious inputs and edge cases, making it more resilient against prompt injection attacks.

  5. Monitoring and Logging: Implement comprehensive monitoring and logging of LLM application interactions. Track prompts received and generated outputs to detect and analyze potential prompt injection attempts, enabling timely response and mitigation.

Examples of Attack Scenarios

  1. Information Disclosure: An attacker crafts a prompt that tricks the LLM into revealing sensitive information, such as user credentials or internal system details. The attacker extracts confidential data by making the model believe the request is legitimate.

  2. Content Filter Bypass: A malicious user bypasses a content filter using specific language patterns, tokens, or encoding mechanisms that the LLM does not recognize as restricted content. This allows the user to perform actions that should be blocked, compromising system integrity and security.

Organizations can enhance the security of their LLM applications and reduce potential risks by implementing strong security measures, conducting frequent testing, and staying vigilant against prompt injection vulnerabilities.

2. Data Leakage

Data leakage is a major concern in Language Model (LM) applications. The accidental or unauthorized release of sensitive information can lead to severe consequences, such as privacy breaches, loss of intellectual property, and non-compliance with regulations.

Common Data Leakage Vulnerabilities

LLM applications can face several data leakage vulnerabilities, including:

  • Improper Handling of Sensitive Data: Inadequate protocols for managing sensitive information within the LM application can result in unintended data exposure. This includes cases where sensitive data is accidentally stored or transmitted in plain text or kept in easily accessible storage locations.

  • Insufficient Access Controls: Weak access controls can allow unauthorized users to access sensitive data. Without proper authentication and authorization mechanisms, the risk of data leakage and unauthorized access increases.

  • Insecure Data Transmission: Poorly secured data transmission channels may expose sensitive information to interception and unauthorized access. Not using encryption protocols or secure communication channels can compromise the confidentiality of data in transit.

How to Prevent Data Leakage

To reduce the risks of data leakage in LM applications, it is crucial to implement strong security measures. Consider these prevention strategies:

  1. Data Classification and Handling: Implement a data classification system to identify and categorize sensitive information within the LM application. Apply appropriate security controls, such as encryption and access restrictions, based on the data's classification level.

  2. Access Controls and User Authentication: Implement strong access controls to ensure that only authorized users can access sensitive data. Use robust user authentication mechanisms, including multi-factor authentication, to prevent unauthorized access and data leakage.

  3. Secure Data Storage: Apply industry-standard encryption techniques to protect sensitive data stored within the LM application. Use secure storage methods and ensure that access to stored data is limited to authorized personnel only.

  4. Secure Data Transmission: Use secure communication protocols, such as HTTPS, for data transmission between the LM application and other systems or users. Encrypt sensitive data during transit to prevent interception and unauthorized access.

  5. Regular Security Audits and Testing: Conduct regular security audits and vulnerability assessments to identify and fix potential weaknesses in the LM application’s data handling processes. Perform penetration testing to evaluate the system's resilience against data leakage attacks.

Examples of Data Leakage Scenarios

  • Misconfigured Access Controls: A misconfigured access control mechanism allows unauthorized users to access sensitive data within the LM application. As a result, confidential information is exposed, leading to potential data leakage and privacy breaches.

  • Insecure Data Transmission: Sensitive data is transmitted over an insecure channel, such as an unencrypted network connection. An attacker intercepts the data during transit, compromising its confidentiality and potentially leading to data leakage.

Organizations can significantly reduce the risk of data leakage in LM applications by implementing strong security measures, such as proper data handling, robust access controls, secure data storage and transmission, and regular security testing.

Proactive measures help ensure the confidentiality, integrity, and availability of sensitive information, protecting against potential data breaches and associated consequences.

3. Inadequate Sandboxing

Inadequate sandboxing practices in Language Model (LM) applications can create significant security risks, making them vulnerable to exploitation by malicious actors.

Inadequate sandboxing refers to the insufficient isolation and containment of the LM application environment. When sandboxing is lacking, the boundaries between the LM and the external systems or resources it interacts with are not well-defined, allowing unauthorized access and potential security breaches.

This lack of proper containment increases the risk of malicious activities, data theft, and unauthorized system manipulation.

Common Vulnerabilities and Risks

Several vulnerabilities and risks are associated with inadequate sandboxing in LLM applications, including:

  • Unauthorized Access: Poor sandboxing can allow unauthorized access to the underlying system, compromising its security. Attackers may exploit vulnerabilities within the LM application to access sensitive resources or execute malicious code.

  • Data Exfiltration: Inadequate sandboxing can enable unauthorized extraction of data from the LM application or its connected systems. Attackers can exploit this vulnerability to steal sensitive information, compromising data confidentiality and integrity.

  • System Manipulation: Insufficient containment can lead to unauthorized manipulation of the LM application’s behavior or the underlying system. Attackers may modify the LM’s behavior, inject malicious code, or tamper with critical configurations, causing disruptions or compromising system stability.

How to Prevent Inadequate Sandboxing

To prevent and mitigate the risks of inadequate sandboxing in LM applications, consider these measures:

  1. Robust Sandbox Implementation: Implement a strong sandboxing mechanism that isolates the LM application from the underlying system and external resources. Use technologies like containerization, virtualization, or sandboxing frameworks to create secure and controlled execution environments.

  2. Access Control and Privilege Separation: Enforce strict access controls and privilege separation within the LM application and its sandboxed environment. Ensure only authorized entities have access to sensitive resources and restrict the execution of privileged operations.

  3. Regular Security Patching and Updates: Keep the sandboxing framework and underlying system up-to-date with the latest security patches and updates. This helps address known vulnerabilities and reduce the risk of exploitation.

  4. Security Monitoring and Auditing: Implement continuous security monitoring and auditing to detect any unauthorized activities or attempts to breach the sandbox. Monitor for suspicious behavior, unusual access patterns, and potential sandbox escape attempts.

Examples of Inadequate Sandboxing

  1. Unauthorized System Access: Inadequate sandboxing allows an attacker to exploit vulnerabilities within the LM application and gain unauthorized access to the underlying system. This can lead to unauthorized data access, system manipulation, and potential compromise of critical resources.

  2. Data Exfiltration: Insufficient sandboxing enables an attacker to extract sensitive data from the LM application or its connected systems. By bypassing containment measures, the attacker can exfiltrate valuable information, leading to data breaches and potential legal and reputational consequences.

Organizations can reduce the risks associated with inadequate sandboxing in LM applications by adopting strong sandboxing techniques, enforcing strict access controls, regularly updating systems, and implementing comprehensive security monitoring.

4. Unauthorized Code Execution

Unauthorized code execution in Language Model (LM) applications is a major security concern, potentially leading to unauthorized system access, data breaches, and severe consequences.

Unauthorized code execution refers to an attacker's ability to run malicious code within the LM application’s environment without proper authorization. This can happen due to vulnerabilities in the application, inadequate input validation, or exploitation of weaknesses in the execution environment. This allows attackers to control the application, compromise system resources, and perform malicious actions.

Common Vulnerabilities and Risks Unauthorized code execution

Several vulnerabilities and risks are associated with unauthorized code execution in LM applications, including:

  • Injection Attacks: Attackers exploit input validation vulnerabilities to inject malicious code into the LM application. This includes SQL injection, OS command injection, and similar techniques. Once executed, this code can lead to unauthorized system access or data manipulation.

  • Remote Code Execution: Vulnerabilities in the LM application’s execution environment, such as deserialization or remote code execution vulnerabilities, allow attackers to execute arbitrary code on the server or underlying system, leading to a complete compromise of the application and system control.

  • Scripting Language Vulnerabilities: LM applications that support scripting languages may be vulnerable to code injection attacks. Attackers can inject malicious scripts, leading to unauthorized code execution and potential system compromise.

How to Prevent Unauthorized Code Execution

To prevent and mitigate the risks of unauthorized code execution in LM applications, consider these measures:

  1. Input Validation and Sanitization: Implement strict input validation and sanitization techniques to prevent injection attacks. Ensure user-provided input is properly validated and any potentially malicious code is sanitized or rejected.

  2. Code Review and Security Testing: Conduct regular code reviews and security testing to identify and address vulnerabilities that may lead to unauthorized code execution. This includes analyzing the application’s dependencies, third-party libraries, and code interacting with external systems.

  3. Principle of Least Privilege: Configure the LM application’s execution environment following the principle of least privilege. Restrict the application’s permissions and privileges to only what is necessary for its intended functionality, minimizing the potential impact of unauthorized code execution.

  4. Patch Management: Keep the LM application and its dependencies up-to-date with the latest security patches and updates. Regularly monitor for security advisories and promptly apply patches to address known vulnerabilities.

  5. Application Whitelisting: Implement application whitelisting techniques to restrict the execution of unauthorized or untrusted code within the LM application’s environment. Only approved and trusted code should be allowed to run.

Examples of Unauthorized Code Execution

  1. SQL Injection Attack: An attacker injects malicious SQL queries into the LM application’s input fields. The application fails to properly validate and sanitize the input, leading to unauthorized code execution within the database, compromising data integrity, and potentially providing access to sensitive information.

  2. Remote Code Execution Vulnerability: A vulnerability in the LM application’s execution environment allows an attacker to remotely execute arbitrary code. By exploiting this vulnerability, the attacker gains unauthorized control over the application and the underlying system, enabling them to perform malicious actions and potentially compromise the entire system.

5. SSRF Vulnerabilities

Server-side request Forgery (SSRF) vulnerabilities pose significant security risks to Language Model (LM) applications, potentially leading to unauthorized data access, information disclosure, and remote code execution.

SSRF vulnerabilities occur when an attacker can manipulate an LM application to make unauthorized requests to internal or external resources on the server’s behalf. This allows the attacker to bypass security controls, access sensitive information, or exploit other vulnerable systems. SSRF vulnerabilities typically arise from inadequate input validation and improper handling of user-supplied URLs or network requests.

Common SSRF Vulnerabilities and Risks

Several vulnerabilities and risks are associated with SSRF in LM applications, including:

  • Unauthorized Data Access: Attackers can exploit SSRF vulnerabilities to access sensitive data that should not be exposed to the LM application. By making requests to internal resources or restricted networks, they can bypass network security controls and retrieve unauthorized data.

  • Information Disclosure: SSRF vulnerabilities can enable attackers to retrieve information from internal systems or external services that may be inadvertently exposed. This can include sensitive configuration data, server responses, or access tokens, leading to potential compromise or exploitation.

  • Remote Code Execution: In some cases, SSRF vulnerabilities can be leveraged to execute arbitrary code on internal systems. Attackers can request specific endpoints or services that allow command execution, leading to unauthorized remote code execution and potential system compromise.

How to Prevent SSRF Vulnerabilities

To prevent and mitigate the risks associated with SSRF vulnerabilities in LM applications, consider the following measures:

  1. Strict Input Validation: Implement rigorous input validation to ensure that user-supplied URLs or network requests are properly sanitized and restricted to authorized resources. Validate input formats, enforce whitelists of allowed domains, and apply proper URL parsing techniques.

  2. Network Access Controls: Implement strict network access controls to restrict outbound requests from the LM application. Utilize firewall rules, network segmentation, or similar techniques to limit access to internal resources and external services.

  3. URL Whitelisting and Filtering: Maintain a comprehensive whitelist of trusted URLs and domains the LM application can access. Implement URL filtering mechanisms to block requests to untrusted or potentially malicious resources.

  4. Secure Configuration of External Service Interaction: Ensure the LM application interacts with external services securely. Avoid passing sensitive data or access tokens as part of the request and utilize secure communication protocols (e.g., HTTPS) whenever possible.

  5. Regular Security Updates: Keep the LM application and its dependencies up-to-date with the latest security patches. Stay informed about known SSRF vulnerabilities in the libraries or frameworks used and apply patches promptly.

Examples of SSRF Vulnerabilities

  1. Internal Network Access: An attacker exploits an SSRF vulnerability in the LM application to make requests to internal network resources, such as databases or administrative interfaces, bypassing network security controls. This unauthorized access can lead to data exfiltration or compromise of critical resources.

  2. Exploiting Trusted Services: An attacker manipulates the LM application to make requests to trusted external services with SSRF vulnerabilities. By leveraging these vulnerabilities, the attacker gains unauthorized access to sensitive information or exploits the target service to execute arbitrary code, compromising the integrity and security of the system.

6. Overreliance on LLM-generated Content

Overreliance on LLM-generated content can introduce significant risks to Language Model (LM) applications, including misinformation, biased outputs, and compromised data integrity.

Overreliance on LLM-generated content refers to excessive dependence on the outputs produced by the language model without sufficient human validation and critical evaluation. While LM applications can provide valuable assistance, they are not infallible and can produce inaccurate, biased, or misleading information. Overreliance on such outputs can lead to misinformation, compromised decision-making, and negative consequences for users and organizations.

Common Overreliance Vulnerabilities and Risks

Several vulnerabilities and risks are associated with overreliance on LLM-generated content, including:

  • Inaccurate Information: LLMs may generate outputs that are factually incorrect or misleading. This can occur due to limitations in training data, biases in the data, or the model’s inability to comprehend context accurately. Organizations and users relying solely on LLM-generated content without validation may propagate false or unreliable information.

  • Bias and Fairness Issues: LLMs can inherit biases present in the training data, leading to biased outputs. Overreliance on such outputs can perpetuate unfair or discriminatory practices, impact decision-making processes, and compromise the trust and inclusivity of the application.

  • Ethical Concerns: Overreliance on LLM-generated content without proper human oversight can raise ethical concerns, such as the dissemination of harmful or offensive content, privacy violations, or the creation of content that violates legal or ethical boundaries.

How to Mitigate Overreliance on LLM-generated Content

To mitigate the risks associated with overreliance on LLM-generated content, it is essential to adopt the following measures:

  1. Human Validation and Critical Evaluation: Incorporate human validation and critical evaluation processes to verify and validate the outputs generated by the LLM. Ensure that trained experts review and assess the content for accuracy, bias, and ethical considerations before it is utilized or shared.

  2. Diverse Training Data: Enhance the diversity and representativeness of the training data used to train the LLM. This helps reduce biases and improves the model’s ability to generate more accurate and fair outputs.

  3. User Education and Awareness: Educate users about the limitations of LLM-generated content and encourage critical thinking when interpreting and utilizing the outputs. Promote awareness of potential biases, inaccuracies, and ethical concerns, enabling users to make informed decisions.

  4. Regular Model Monitoring and Updates: Continuously monitor the performance of the LLM and update the model as new data and techniques become available. Regularly assess and address biases, inaccuracies, and other shortcomings to improve the overall quality and reliability of the generated content.

Examples of Overreliance on LLM-generated Content

  1. Dissemination of Misinformation: An organization solely relies on LLM-generated content to publish news articles without human verification. The content contains inaccuracies, leading to the dissemination of misinformation and eroding the organization’s credibility.

  2. Biased Decision-making: A company utilizes an LLM-based system to automate hiring decisions. However, the system exhibits biases towards certain demographics due to the biases present in the training data. Overreliance on the system’s outputs leads to unfair hiring practices and potential legal consequences.

7. Inadequate AI Alignment

Inadequate AI alignment presents significant risks to Language Model (LM) applications, including biased outputs, ethical concerns, and unintended consequences.

Inadequate AI alignment refers to a misalignment between an AI system's objectives and values and those of its human users. When LM applications lack proper alignment, they can produce outputs that deviate from desired outcomes, exhibit biases, or generate content that conflicts with ethical considerations. Inadequate AI alignment can result from biases in training data, flawed objective functions, or insufficient consideration of societal impacts.

Common Inadequate AI Alignment Vulnerabilities and Risks

Several vulnerabilities and risks are associated with inadequate AI alignment in LM applications, including:

  1. Biased Outputs: Inadequate AI alignment can lead to biased outputs that favour certain groups or perspectives, perpetuating unfairness and discrimination. Biases present in training data or flawed objective functions can influence the generated content, compromising the fairness and integrity of the application.

  2. Ethical Concerns: LM applications with inadequate AI alignment may produce content that violates ethical principles or societal norms. This can include generating offensive, harmful, or misleading information, leading to reputational damage and potential legal consequences.

  3. Unintended Consequences: Inadequate AI alignment can lead to inadequate consideration of long-term societal impacts and unintended consequences. LM applications may inadvertently promote harmful behaviors, reinforce stereotypes, or amplify divisive content, contributing to social polarization and discord.

How to Ensure Adequate AI Alignment

To ensure adequate AI alignment in LM applications, it is crucial to implement the following measures:

  1. Ethical Frameworks and Guidelines: Establish clear ethical frameworks and guidelines for developing and deploying LM applications. These should include fairness, transparency, accountability, and inclusivity principles, aligning the AI system with human values and societal expectations.

  2. Bias Detection and Mitigation: Implement mechanisms to detect and mitigate biases in LM-generated content. This includes diverse training data, regular bias audits, and bias correction techniques to minimize the impact of biased outputs.

  3. Human-in-the-Loop Approach: Incorporate human oversight and involvement in the decision-making process of the LM application. Human reviewers and experts can provide feedback, evaluate outputs for alignment with human values, and identify potential ethical concerns.

  4. Transparency and Explainability: Enhance the LM application's transparency and explainability by providing insights into how the system generates outputs. This enables users to understand the decision-making process and promotes trust, accountability, and better alignment with user expectations.

  5. Continuous Evaluation and Feedback: Continuously evaluate the LM application's performance and impact, seeking feedback from users and stakeholders. This feedback loop helps identify and address alignment issues, refine the model, and adapt to evolving user needs and ethical considerations.

Examples of Inadequate AI Alignment

  1. Biased Content Generation: An LM application generates content that consistently favours a particular political ideology due to biases in the training data. Inadequate AI alignment results in outputs that reflect a skewed perspective, undermining the application’s credibility and user trust.

  2. Harmful Recommendations: An LM-powered recommendation system fails to consider the long-term impacts of its suggestions. It inadvertently promotes unhealthy or dangerous behaviors, compromising user safety and well-being. Inadequate AI alignment neglects the responsibility of aligning recommendations with user health and welfare.

8. Insufficient Access Controls

Insufficient access controls refer to the inadequate protection of resources and functionalities within an LM application. When access controls are weak or improperly configured, unauthorized individuals or entities may gain unauthorized access to sensitive data, manipulate the system, or perform actions beyond their privileges. This can result in unauthorized disclosure, data breaches, or the compromise of critical system components.

Common Insufficient Access Controls Vulnerabilities and Risks

Several vulnerabilities and risks are associated with insufficient access controls in LM applications, including:

  1. Unauthorized Access: Insufficient access controls can allow unauthorized individuals to gain access to sensitive information or functionalities within the LM application. This can lead to data theft, unauthorized modifications, or unauthorized use of system resources.

  2. Privilege Escalation: Inadequate access controls may enable unauthorized users to escalate their privileges within the LM application. They can gain administrative or elevated privileges, granting them unauthorized control over critical system components or access to sensitive data.

  3. Data Breaches: Weak access controls can expose sensitive data to unauthorized individuals. This includes personally identifiable information (PII), financial data, or proprietary information. Data breaches can result in financial losses, reputational damage, and legal implications.

How to Enhance Access Controls

To enhance access controls in LM applications and mitigate associated risks, the following measures should be implemented:

  1. Role-Based Access Control (RBAC): Implement RBAC mechanisms to assign roles and associated privileges to users based on their responsibilities and job functions. This ensures that users only have access to the resources and functionalities necessary to perform their tasks.

  2. Strong Authentication and Authorization: Enforce strong authentication mechanisms, such as multi-factor authentication (MFA), to verify users' identities. Additionally, robust authorization mechanisms should be implemented to control access to different resources and functionalities based on user roles and privileges.

  3. Regular Access Reviews: Conduct regular access reviews to ensure that user privileges are up to date and aligned with their job requirements. Remove or modify access rights promptly when employees change roles or leave the organization.

  4. Least Privilege Principle: Follow the principle of least privilege, granting users the minimum privileges required to perform their tasks effectively. Restrict access to sensitive data and critical system components to authorized personnel only.

  5. Monitoring and Auditing: Implement robust monitoring and auditing mechanisms to track user activities within the LM application. This includes logging and analyzing access attempts, detecting anomalies, and promptly responding to any suspicious activities.

Examples of Insufficient Access Controls

  1. Unauthorized Data Access: A user with lower-level privileges gains unauthorized access to a database containing sensitive customer information. Insufficient access controls allow the user to view, modify, or extract sensitive data, potentially leading to identity theft or fraud.

  2. Privilege Escalation: An attacker exploits a vulnerability in the LM application to escalate their privileges from a regular user to an administrator. Insufficient access controls enable the attacker to gain unauthorized control over critical system components and compromise the application’s integrity.

9. Improper Error Handling

Improper error handling in Language Model (LM) applications can introduce vulnerabilities and impact overall functionality, security, and user experience.

Improper error handling refers to the inadequate handling of exceptions, errors, and unexpected events within an LM application. When errors are not handled appropriately, they can lead to application crashes, unexpected behavior, data corruption, or security vulnerabilities. Proper error handling is crucial for maintaining the stability, reliability, and security of LM applications.

Common Improper Error Handling Vulnerabilities and Risks

Several vulnerabilities and risks are associated with improper error handling in LM applications, including:

  1. Application Crashes: Insufficient error handling can cause unhandled exceptions or errors, resulting in application crashes or unintended terminations. This can lead to loss of user data, disrupted workflows, and negative user experiences.

  2. Information Disclosure: Improper error handling may inadvertently reveal sensitive information in error messages or debug logs. Attackers can exploit this information to gain insights into the application’s internal workings, potentially leading to further security breaches or system compromise.

  3. Denial of Service (DoS): Inadequate error handling can be exploited by malicious actors to launch denial-of-service attacks against the LM application. By deliberately triggering errors or exceptions, attackers can overload system resources, causing service disruptions for legitimate users.

How to Implement Proper Error Handling

To implement proper error handling in LM applications and mitigate associated risks, the following measures should be implemented:

  1. Exception Handling: Implement comprehensive exception-handling mechanisms to catch and handle exceptions effectively. Properly logging exceptions, providing informative error messages, and gracefully recovering from errors help maintain application stability and prevent crashes.

  2. Error Logging and Monitoring: Establish robust error logging and monitoring capabilities to capture and analyze application errors. This helps identify recurring issues, track error patterns, and enable timely resolution of potential vulnerabilities or bugs.

  3. User-Friendly Error Messages: Craft user-friendly error messages that provide relevant information without disclosing sensitive data. Avoid exposing internal implementation details or stack traces that could be exploited by attackers.

  4. Input Validation and Sanitization: Validate and sanitize user input to prevent errors caused by invalid or malicious data. Proper input validation helps ensure data integrity, mitigates the risk of injection attacks, and reduces the occurrence of runtime errors.

  5. Fail-Safe Mechanisms: Implement fail-safe mechanisms to handle exceptional situations or unexpected errors gracefully. This can include fallback strategies, alternative paths, or automatic recovery procedures to minimize disruptions and maintain application availability.

Examples of Improper Error Handling Application

  1. Crash on Invalid Input: An LM application crashes when it encounters unexpected or invalid input from a user. The crash occurs due to inadequate input validation and error handling, disrupting user workflows and potentially leading to data loss.

  2. Information Disclosure in Error Messages: Error messages in an LM application inadvertently disclose sensitive information, such as database connection strings or internal file paths. This improper error handling exposes critical details to potential attackers, increasing the risk of unauthorized access or data breaches.

10. Training Data Poisoning

Training data poisoning poses a significant threat to Language Model (LM) applications, potentially leading to compromised model performance, biased outputs, or malicious behavior.

Training data poisoning refers to the deliberate manipulation or injection of malicious or biased data into the training process of an LM. This can lead to the model learning undesirable behaviors, producing biased or harmful outputs, or being vulnerable to adversarial attacks. Training data poisoning can compromise the fairness, accuracy, and security of LM applications.

Common Training Data Poisoning Vulnerabilities and Risks

Several vulnerabilities and risks are associated with training data poisoning in LM applications, including:

  1. Bias Amplification: Poisoned training data can introduce or amplify biases present in the data, leading to biased outputs or discriminatory behavior by the LM application. This can perpetuate social biases, reinforce stereotypes, or discriminate against certain individuals or groups.

  2. Malicious Behavior Induction: Poisoned data can be strategically crafted to induce the LM model to exhibit malicious behavior. This can include generating harmful or offensive content, promoting misinformation, or manipulating user interactions.

  3. Adversarial Attacks: Adversaries can intentionally inject poisoned data to exploit vulnerabilities in the LM model and bypass security measures. This can lead to attacks such as data exfiltration, unauthorized access, or the manipulation of system outputs.

How to Prevent Training Data Poisoning

To prevent training data poisoning and mitigate associated risks, the following measures should be implemented:

  1. Data Quality Assurance: Establish rigorous data quality assurance processes to ensure the integrity and reliability of training data. This includes thorough data validation, cleaning, and verification to identify and remove any potentially poisoned or biased samples.

  2. Data Diversity and Representativeness: Ensure training data are diverse, representative, and free from explicit or implicit biases. Incorporate multiple perspectives and sources to mitigate the risk of biases being amplified during the training process.

  3. Adversarial Data Detection: Employ techniques to detect and mitigate adversarial data injections during the training phase. This can involve anomaly detection, robust statistical analysis, or leveraging adversarial machine learning methods to identify and remove poisoned samples.

  4. Regular Model Monitoring and Evaluation: Continuously monitor and evaluate the performance and behavior of the trained LM model. This includes analyzing outputs, identifying biases, and addressing any emerging issues or suspicious patterns in the model’s behavior.

Examples of Training Data Poisoning Bias

  1. Amplification in Text Classification: A training dataset for an LM used in text classification contains biased samples that disproportionately favor one group over another. As the model learns from this data, it amplifies the biases, leading to biased classifications and discriminatory outputs.

  2. Adversarial Attack through Poisoned Data: An adversary injects poisoned data samples into the training dataset, aiming to manipulate the LM model’s behavior. The injected samples exploit vulnerabilities in the model, allowing the adversary to bypass security measures, gain unauthorized access, or deceive the system.


OWASP is a reliable guide in the fight against security threats posed by large language models (LLMs). Their comprehensive plan provides us with the knowledge and strategies to safeguard LLM applications.

OWASP highlights the unique vulnerabilities of LLMs, such as prompt injections, data leakage, inadequate sandboxing, and unauthorized code execution. Their strategy focuses on prevention and mitigation to ensure data confidentiality and integrity.

Enforcing strict input validation, context-aware filtering, and regular updates strengthens our defenses. Implementing robust sandboxing, proper access controls, and effective error handling builds strong barriers against potential breaches.