Introduction: What Are Cybersecurity Policies and Procedures?
Cybersecurity policies and procedures are vital to any successful information security strategy. A cybersecurity policy is a document that outlines clear expectations, rules, and the approach that an organization uses to maintain integrity, confidentiality, and availability of sensitive information.
A comprehensive cybersecurity policy defines the IT systems and data assets that must be protected, the threats to these assets, and the rules guiding the protection of the assets.
Whether you’re building a cybersecurity team or setting security standards for your employees, outlining cybersecurity policies is crucial, especially in the modern business world. This is because cyber-attacks have increased over the years. As such, security policies help each employee understand their role in protecting your organization’s IT systems and data assets.
Without a cybersecurity policy, an organization becomes vulnerable to cyberattacks and data security risks, resulting in the loss of consumer data. If deemed negligent, your organization could face legal issues and substantial financial losses.
Are you in the process of creating network security standards? Here is a complete guide on how to develop cybersecurity policies and procedures for your organization.
Why Security Policies Are Important
Besides the risk of disastrous data breaches, cybersecurity policies and procedures are essential for many other reasons:
- Cybersecurity policies help organizations set clear expectations and security standards for each employee, user, or department. For example, your company’s cybersecurity policy may set well-defined guidelines regarding encryption of email attachments, sharing of passwords, installing unapproved software, using company devices for personal use, or restrictions for social media usage.
- Security policies also ensure organizations meet regulatory and compliance requirements. This includes the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), Information Security Management System (ISO 27001), Payment Card Industry Data Security Standard (PCI-DSS), and SOC2.
- Information security procedures regulate how technical controls are implemented across all levels of an organization. For example, a security policy could cover access control standards and authentication systems.
- By setting out standards for the security program, everyone understands their responsibilities and what to do in certain events. This improves organization efficiency, facilitates smoother workflows, and helps you achieve business goals.
Before developing a cybersecurity policy, you must understand the types of policies and which one your organization needs.
Types of Cybersecurity Policies and Procedures
Cybersecurity policies depend on the organization's needs. This means they can differ in scope, applicability, and complexity. Security policies can have different objectives and address unique issues.
Although there is no single model, the National Institute of Standards and Technology (NIST) identifies three criteria for classifying security policies:
Program Policies
Also called a master policy, a program policy is essentially a security program plan for the entire organization. It is a clear and well-defined blueprint that establishes security objectives and execution procedures to ensure overall security.
Program policies define the goals of the entire organization's cybersecurity team and plan, including compliance mechanisms and the responsibilities of each employee. A master policy is scarcely changed or updated because they are strategically written to remain relevant irrespective of technological and organizational transformation.
Examples of program policies are:
- Patent formulas
- Frequency of upgrading antivirus software and installing security updates
- Data backup
- Malicious software
- Disaster recovery plans
- Password management and construction, etc.
Issue-Specific Policy
This type of security policy addresses certain operational concerns. An issue-specific policy outlines a specific issue and the relevant security procedures that go with it.
Instructions are sent to the appropriate employees or cybersecurity team members to help resolve the problem. Examples of issue-specific policies are:
- Email security
- Social media usage measures
- Bring-Your-Own-Device (BYOD)
- Remote access
- Wireless security and Bluetooth policies.
System-Specific Policy
A system-specific cybersecurity policy focuses on specific systems such as a web server, firewall, or even a single computer. According to NIST, system-specific policies should include a security goal and operational guidelines.
As the most detailed type of cybersecurity policy and procedure, system-specific policies involve all IT and security teams. However, the executive still makes the major decisions and rules.
Examples of system-specific policies are:
- Server security
- Workstation
- Application security
- Database policies, etc.
Cybersecurity policies have evolved recently, especially with the increased cyber-attack rate. Employee-specific policies are a type of cybersecurity policy that focuses on developing your employees' security skills and awareness.
According to a Haystax survey, employees are the number one cause of data breaches through carelessness or negligence of security policies. Employees are more likely to click on malicious URLs, forget to encrypt sensitive documents, and use unverified cloud applications. Cybrary provides team-focused training to 96% of the Fortune 1000 to help develop comprehensive cybersecurity policies. Publicly traded companies in regulated industries like Healthcare, Insurance, and Finance usually face the most threats. Individuals and teams in these companies can learn IT security policy on Cybrary to strengthen their organization’s data security frameworks, supply chain management, and legal concepts.
Elements of a Cybersecurity Policy
Here are the essential components of any cybersecurity policy:
1. Have Well-Defined and Realistic Objectives
Every cybersecurity policy must have a clear purpose and realistic objectives. The goal of your organization's policy must be well-defined so that each employee, team member, and department understands its importance and responsibilities.
Cybersecurity policies and procedures must also be realistic and enforceable. They shouldn't be excessively burdensome, nor should they be thin.
2. Establish the Scope and Applicability
It’s essential to state the scope or applicability of your security policy. Information security policies and procedures must outline who they apply to and under what circumstances. This also covers the geographic region, which is especially important for remote teams.
3. Use Non-Technical Terms for Program Policies
Most cybersecurity policies and procedures require concise language because the audience is usually non-technical. This is especially true for program policies. System-specific policies may include technical jargon, but generally, policies should be easily understandable.
4. Update Based on Growing Risks
Cybersecurity threats are growing daily. While program policies are not frequently updated, they should be flexible enough to remain relevant. Other cybersecurity policies must be regularly reviewed to ensure the organization remains safe from attacks.
5. Have it in Writing
Cybersecurity policies and procedures should be documented. Every concerned staff member must read, understand, and sign the policy. New hires must be required to read and confirm their understanding.
This security policy isn't a set of voluntary guidelines but an employment condition. Hence, there should be clear penalties for breaches in security policy.
How to Develop Cybersecurity Policies and Procedures
When developing an information security policy, ensure it guides all employees on the following:
- The type of information that can be shared and where they can be shared.
- The acceptable devices and online materials to be used.
- Handling and storing sensitive business data, material, and other confidential assets.
In most organizations, the Chief Information Security Officer (CISO) leads the development of cybersecurity policies and procedures. If you want to create a robust network security procedure, here is a complete cybersecurity compliance checklist to get you started:
1. Identify Your Organization’s Security Risks, Assets, and Threats
It is vital to identify and prioritize your assets, as well as the potential risks or threats that these assets may suffer. These questions will help you establish potential risks and the assets that should be prioritized:
- What risks or threats does your company currently face?
- Are there information and data assets that should be restricted?
- Do your employees send or receive many large files and attachments?
- Which security threats will damage your organization the most?
Teams should begin with a cybersecurity risk assessment to identify vulnerabilities and areas of concern that could lead to a data breach.
Understanding the organization's tolerance for various security threats is critical. This also includes identifying which concerns are low risk and which endanger the organization's sustainability.
2. Establish Password Requirements Across the Organization
Employees are more likely to be targeted by cybercriminals due to security negligence, such as weak passwords.
Therefore, password management and construction policies must form part of your overall IT security policy. It should have cybersecurity procedures on:
- Password creation and requirements for strong passphrases
- The right way to store passwords and update frequency
- The importance of unique passwords for different logins
It should also contain the type of authentication that is required for different user accounts.
3. Provide Designated Email Security Measures
Cybersecurity policies and procedures must contain designated email security measures across all units. This includes guidelines for sharing work email addresses, opening email attachments from trusted business contacts only, deleting and reporting spam emails, and preventing phishing.
4. Outline Procedures to Handle Sensitive Data
When developing a cybersecurity policy, it’s important to clearly define what sensitive data is and how it must be handled. Data security policies must contain sharing permissions and data masking techniques during a threat. In addition, the policy must include how employees should store physical files containing sensitive data.
5. Set Standards for Handling Technology, Social Media, and Internet Usage
Cybersecurity standard operating procedures for handling technology are essential, especially for remote teams. Your cybersecurity policy must establish guidelines on the following:
- Where to access devices when not physically at work.
- How devices that are not in use should be shut down and stored
- Steps to report the loss of a work device
- Protecting data on secondary storage or removable devices like USB sticks
- System updates on personal computers
- Data scanning and protection
- Locking device screens when they are not in use
6. Develop Cybersecurity Response Plans
Dread it, run from it, but you’ll probably still face cyber-attacks at some point. That’s why every cybersecurity policy must contain what steps each user must take in the event of a cyber-attack. Hence, the security policy must cover procedures, response actions, and incident handling.
7. Ensure Your Policy Meets Compliance and Regulatory Requirements
Implementing a cybersecurity policy doesn't guarantee it will pass a compliance check. There are regulations you must follow regarding companies. Your cybersecurity team, especially Privacy Officers, must consider regulatory requirements and ensure your security policy meets compliance and federal government standards.
8. Test Run Your Cybersecurity Policy
You shouldn't wait for cyber-attacks, or other data breach attempts to evaluate the effectiveness of your security policy. This is why it's crucial to have Penetration Testers and Ethical Hackers in your cybersecurity team. These professionals will help conduct regular cybersecurity risk assessments such as Incident Response Tabletop Exercises and Ransomware Readiness Assessments. This is the only way to determine if your chosen cybersecurity policies and procedures are adequate in real-world scenarios.
9. Update Guidelines Regularly
It’s important to work with senior management officials, the IT team, and other relevant departments to update cybersecurity policies collectively. As security trends evolve, your organization must keep an eye out for the latest threats in the industry and update its security infrastructure.
10. Train Your Employees
Although updating your security policies is an effective way to avoid new threats, you shouldn't neglect your employees. They are the ones that will operate new security technologies and methodologies, making it essential to train them.
Before you continue An effective cybersecurity policy is critical to the reputation and survival of your company. It will help provide comprehensive threat protection and ensure immediate recovery after security incidents.
Understanding the appropriate cybersecurity policies and training your staff has never been easier. Cybrary offers a suite of hands-on learning options to significantly improve your information security infrastructure, from program and issue-specific policies to system and employee-centric policies. Start for free now.
The Open Worldwide Application Security Project (OWASP) is a community-led organization and has been around for over 20 years and is largely known for its Top 10 web application security risks (check out our course on it). As the use of generative AI and large language models (LLMs) has exploded recently, so too has the risk to privacy and security by these technologies. OWASP, leading the charge for security, has come out with its Top 10 for LLMs and Generative AI Apps this year. In this blog post we’ll explore the Top 10 risks and explore examples of each as well as how to prevent these risks.
LLM01: Prompt Injection
Those familiar with the OWASP Top 10 for web applications have seen the injection category before at the top of the list for many years. This is no exception with LLMs and ranks as number one. Prompt Injection can be a critical vulnerability in LLMs where an attacker manipulates the model through crafted inputs, leading it to execute unintended actions. This can result in unauthorized access, data exfiltration, or social engineering. There are two types: Direct Prompt Injection, which involves "jailbreaking" the system by altering or revealing underlying system prompts, giving an attacker access to backend systems or sensitive data, and Indirect Prompt Injection, where external inputs (like files or web content) are used to manipulate the LLM's behavior.
As an example, an attacker might upload a resume containing an indirect prompt injection, instructing an LLM-based hiring tool to favorably evaluate the resume. When an internal user runs the document through the LLM for summarization, the embedded prompt makes the LLM respond positively about the candidate’s suitability, regardless of the actual content.
How to prevent prompt injection:
- Limit LLM Access: Apply the principle of least privilege by restricting the LLM's access to sensitive backend systems and enforcing API token controls for extended functionalities like plugins.
- Human Approval for Critical Actions: For high-risk operations, require human validation before executing, ensuring that the LLM's suggestions are not followed blindly.
- Separate External and User Content: Use frameworks like ChatML for OpenAI API calls to clearly differentiate between user prompts and untrusted external content, reducing the chance of unintentional action from mixed inputs.
- Monitor and Flag Untrusted Outputs: Regularly review LLM outputs and mark suspicious content, helping users to recognize potentially unreliable information.
LLM02: Insecure Output Handling
Insecure Output Handling occurs when the outputs generated by a LLM are not properly validated or sanitized before being used by other components in a system. Since LLMs can generate various types of content based on input prompts, failing to handle these outputs securely can introduce risks like cross-site scripting (XSS), server-side request forgery (SSRF), or even remote code execution (RCE). Unlike Overreliance (LLM09), which focuses on the accuracy of LLM outputs, Insecure Output Handling specifically addresses vulnerabilities in how these outputs are processed downstream.
As an example, there could be a web application that uses an LLM to summarize user-provided content and renders it back in a webpage. An attacker submits a prompt containing malicious JavaScript code. If the LLM’s output is displayed on the webpage without proper sanitization, the JavaScript will execute in the user’s browser, leading to XSS. Alternatively, if the LLM’s output is sent to a backend database or shell command, it could allow SQL injection or remote code execution if not properly validated.
How to prevent Insecure Output Handling:
- Zero-Trust Approach: Treat the LLM as an untrusted source, applying strict allow list validation and sanitization to all outputs it generates, especially before passing them to downstream systems or functions.
- Output Encoding: Encode LLM outputs before displaying them to end users, particularly when dealing with web content where XSS risks are prevalent.
- Adhere to Security Standards: Follow the OWASP Application Security Verification Standard (ASVS) guidelines, which provide strategies for input validation and sanitization to protect against code injection risks.
LLM03: Training Data Poisoning
Training Data Poisoning refers to the manipulation of the data used to train LLMs, introducing biases, backdoors, or vulnerabilities. This tampered data can degrade the model's effectiveness, introduce harmful biases, or create security flaws that malicious actors can exploit. Poisoned data could lead to inaccurate or inappropriate outputs, compromising user trust, harming brand reputation, and increasing security risks like downstream exploitation.
As an example, there could be a scenario where an LLM is trained on a dataset that has been tampered with by a malicious actor. The poisoned dataset includes subtly manipulated content, such as biased news articles or fabricated facts. When the model is deployed, it may output biased information or incorrect details based on the poisoned data. This not only degrades the model’s performance but can also mislead users, potentially harming the model’s credibility and the organization’s reputation.
How to prevent Training Data Poisoning:
- Data Validation and Vetting: Verify the sources of training data, especially when sourcing from third-party datasets. Conduct thorough checks on data integrity, and where possible, use trusted data sources.
- Machine Learning Bill of Materials (ML-BOM): Maintain an ML-BOM to track the provenance of training data and ensure that each source is legitimate and suitable for the model’s purpose.
- Sandboxing and Network Controls: Restrict access to external data sources and use network controls to prevent unintended data scraping during training. This helps ensure that only vetted data is used for training.
- Adversarial Robustness Techniques: Implement strategies like federated learning and statistical outlier detection to reduce the impact of poisoned data. Periodic testing and monitoring can identify unusual model behaviors that may indicate a poisoning attempt.
- Human Review and Auditing: Regularly audit model outputs and use a human-in-the-loop approach to validate outputs, especially for sensitive applications. This added layer of scrutiny can catch potential issues early.
LLM04: Model Denial of Service
Model Denial of Service (DoS) is a vulnerability in which an attacker deliberately consumes an excessive amount of computational resources by interacting with a LLM. This can result in degraded service quality, increased costs, or even system crashes. One emerging concern is manipulating the context window of the LLM, which refers to the maximum amount of text the model can process at once. This makes it possible to overwhelm the LLM by exceeding or exploiting this limit, leading to resource exhaustion.
As an example, an attacker may continuously flood the LLM with sequential inputs that each reach the upper limit of the model’s context window. This high-volume, resource-intensive traffic overloads the system, resulting in slower response times and even denial of service. As another example, if an LLM-based chatbot is inundated with a flood of recursive or exceptionally long prompts, it can strain computational resources, causing system crashes or significant delays for other users.
How to prevent Model Denial of Service:
- Rate Limiting: Implement rate limits to restrict the number of requests from a single user or IP address within a specific timeframe. This reduces the chance of overwhelming the system with excessive traffic.
- Resource Allocation Caps: Set caps on resource usage per request to ensure that complex or high-resource requests do not consume excessive CPU or memory. This helps prevent resource exhaustion.
- Input Size Restrictions: Limit input size according to the LLM's context window capacity to prevent excessive context expansion. For example, inputs exceeding a predefined character limit can be truncated or rejected.
- Monitoring and Alerts: Continuously monitor resource utilization and establish alerts for unusual spikes, which may indicate a DoS attempt. This allows for proactive threat detection and response.
- Developer Awareness and Training: Educate developers about DoS vulnerabilities in LLMs and establish guidelines for secure model deployment. Understanding these risks enables teams to implement preventative measures more effectively.
LLM05: Supply Chain Vulnerabilities
Supply Chain attacks are incredibly common and this is no different with LLMs, which, in this case refers to risks associated with the third-party components, training data, pre-trained models, and deployment platforms used within LLMs. These vulnerabilities can arise from outdated libraries, tampered models, and even compromised data sources, impacting the security and reliability of the entire application. Unlike traditional software supply chain risks, LLM supply chain vulnerabilities extend to the models and datasets themselves, which may be manipulated to include biases, backdoors, or malware that compromises system integrity.
As an example, an organization uses a third-party pre-trained model to conduct economic analysis. If this model is poisoned with incorrect or biased data, it could generate inaccurate results that mislead decision-making. Additionally, if the organization uses an outdated plugin or compromised library, an attacker could exploit this vulnerability to gain unauthorized access or tamper with sensitive information. Such vulnerabilities can result in significant security breaches, financial loss, or reputational damage.
How to prevent Supply Chain Vulnerabilities:
- Vet Third-Party Components: Carefully review the terms, privacy policies, and security measures of all third-party model providers, data sources, and plugins. Use only trusted suppliers and ensure they have robust security protocols in place.
- Maintain a Software Bill of Materials (SBOM): An SBOM provides a complete inventory of all components, allowing for quick detection of vulnerabilities and unauthorized changes. Ensure that all components are up-to-date and apply patches as needed.
- Use Model and Code Signing: For models and external code, employ digital signatures to verify their integrity and authenticity before use. This helps ensure that no tampering has occurred.
- Anomaly Detection and Robustness Testing: Conduct adversarial robustness tests and anomaly detection on models and data to catch signs of tampering or data poisoning. Integrating these checks into your MLOps pipeline can enhance overall security.
- Implement Monitoring and Patching Policies: Regularly monitor component usage, scan for vulnerabilities, and patch outdated components. For sensitive applications, continuously audit your suppliers’ security posture and update components as new threats emerge.
LLM06: Sensitive Information Disclosure
Sensitive Information Disclosure in LLMs occurs when the model inadvertently reveals private, proprietary, or confidential information through its output. This can happen due to the model being trained on sensitive data or because it memorizes and later reproduces private information. Such disclosures can result in significant security breaches, including unauthorized access to personal data, intellectual property leaks, and violations of privacy laws.
As an example, there could be an LLM-based chatbot trained on a dataset containing personal information such as users’ full names, addresses, or proprietary business data. If the model memorizes this data, it could accidentally reveal this sensitive information to other users. For instance, a user might ask the chatbot for a recommendation, and the model could inadvertently respond with personal information it learned during training, violating privacy rules.
How to prevent Sensitive Information Disclosure:
- Data Sanitization: Before training, scrub datasets of personal or sensitive information. Use techniques like anonymization and redaction to ensure no sensitive data remains in the training data.
- Input and Output Filtering: Implement robust input validation and sanitization to prevent sensitive data from entering the model’s training data or being echoed back in outputs.
- Limit Training Data Exposure: Apply the principle of least privilege by restricting sensitive data from being part of the training dataset. Fine-tune the model with only the data necessary for its task, and ensure high-privilege data is not accessible to lower-privilege users.
- User Awareness: Make users aware of how their data is processed by providing clear Terms of Use and offering opt-out options for having their data used in model training.
- Access Controls: Apply strict access control to external data sources used by the LLM, ensuring that sensitive information is handled securely throughout the system
LLM07: Insecure Plugin Design
Insecure Plugin Design vulnerabilities arise when LLM plugins, which extend the model’s capabilities, are not adequately secured. These plugins often allow free-text inputs and may lack proper input validation and access controls. When enabled, plugins can execute various tasks based on the LLM’s outputs without further checks, which can expose the system to risks like data exfiltration, remote code execution, and privilege escalation. This vulnerability is particularly dangerous because plugins can operate with elevated permissions while assuming that user inputs are trustworthy.
As an example, there could be a weather plugin that allows users to input a base URL and query. An attacker could craft a malicious input that directs the LLM to a domain they control, allowing them to inject harmful content into the system. Similarly, a plugin that accepts SQL “WHERE” clauses without validation could enable an attacker to execute SQL injection attacks, gaining unauthorized access to data in a database.
How to prevent Insecure Plugin Design:
- Enforce Parameterized Input: Plugins should restrict inputs to specific parameters and avoid free-form text wherever possible. This can prevent injection attacks and other exploits.
- Input Validation and Sanitization: Plugins should include robust validation on all inputs. Using Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) can help identify vulnerabilities during development.
- Access Control: Follow the principle of least privilege, limiting each plugin's permissions to only what is necessary. Implement OAuth2 or API keys to control access and ensure only authorized users or components can trigger sensitive actions.
- Manual Authorization for Sensitive Actions: For actions that could impact user security, such as transferring files or accessing private repositories, require explicit user confirmation.
- Adhere to OWASP API Security Guidelines: Since plugins often function as REST APIs, apply best practices from the OWASP API Security Top 10. This includes securing endpoints and applying rate limiting to mitigate potential abuse.
LLM08: Excessive Agency
Excessive Agency in LLM-based applications arises when models are granted too much autonomy or functionality, allowing them to perform actions beyond their intended scope. This vulnerability occurs when an LLM agent has access to functions that are unnecessary for its purpose or operates with excessive permissions, such as being able to modify or delete records instead of only reading them. Unlike Insecure Output Handling, which deals with the lack of validation on the model’s outputs, Excessive Agency pertains to the risks involved when an LLM takes actions without proper authorization, potentially leading to confidentiality, integrity, and availability issues.
As an example, there could be an LLM-based assistant that is given access to a user's email account to summarize incoming messages. If the plugin that is used to read emails also has permissions to send messages, a malicious prompt injection could trick the LLM into sending unauthorized emails (or spam) from the user's account.
How to prevent Excessive Agency:
- Restrict Plugin Functionality: Ensure plugins and tools only provide necessary functions. For example, if a plugin is used to read emails, it should not include capabilities to delete or send emails.
- Limit Permissions: Follow the principle of least privilege by restricting plugins’ access to external systems. For instance, a plugin for database access should be read-only if writing or modifying data is not required.
- Avoid Open-Ended Functions: Avoid functions like “run shell command” or “fetch URL” that provide broad system access. Instead, use plugins that perform specific, controlled tasks.
- User Authorization and Scope Tracking: Require plugins to execute actions within the context of a specific user's permissions. For example, using OAuth with limited scopes helps ensure actions align with the user’s access level.
- Human-in-the-Loop Control: Require user confirmation for high-impact actions. For instance, a plugin that posts to social media should require the user to review and approve the content before it is published.
- Authorization in Downstream Systems: Implement authorization checks in downstream systems that validate each request against security policies. This prevents the LLM from making unauthorized changes directly.
LLM09: Overreliance
Overreliance occurs when users or systems trust the outputs of a LLM without proper oversight or verification. While LLMs can generate creative and informative content, they are prone to “hallucinations” (producing false or misleading information) or providing authoritative-sounding but incorrect outputs. Overreliance on these models can result in security risks, misinformation, miscommunication, and even legal issues, especially if LLM-generated content is used without validation. This vulnerability becomes especially dangerous in cases where LLMs suggest insecure coding practices or flawed recommendations.
As an example, there could be a development team using an LLM to expedite the coding process. The LLM suggests an insecure code library, and the team, trusting the LLM, incorporates it into their software without review. This introduces a serious vulnerability. As another example, a news organization might use an LLM to generate articles, but if they don’t validate the information, it could lead to the spread of disinformation.
How to prevent Overreliance:
- Regular Monitoring and Review: Implement processes to review LLM outputs regularly. Use techniques like self-consistency checks or voting mechanisms to compare multiple model responses and filter out inconsistencies.
- Cross-Verification: Compare the LLM’s output with reliable, trusted sources to ensure the information’s accuracy. This step is crucial, especially in fields where factual accuracy is imperative.
- Fine-Tuning and Prompt Engineering: Fine-tune models for specific tasks or domains to reduce hallucinations. Techniques like parameter-efficient tuning (PET) and chain-of-thought prompting can help improve the quality of LLM outputs.
- Automated Validation: Use automated validation tools to cross-check generated outputs against known facts or data, adding an extra layer of security.
- Risk Communication: Clearly communicate the limitations of LLMs to users, highlighting the potential for errors. Transparent disclaimers can help manage user expectations and encourage cautious use of LLM outputs.
- Secure Coding Practices: For development environments, establish guidelines to prevent the integration of potentially insecure code. Avoid relying solely on LLM-generated code without thorough review.
LLM10: Model Theft
Model Theft refers to the unauthorized access, extraction, or replication of proprietary LLMs by malicious actors. These models, containing valuable intellectual property, are at risk of exfiltration, which can lead to significant economic and reputational loss, erosion of competitive advantage, and unauthorized access to sensitive information encoded within the model. Attackers may steal models directly from company infrastructure or replicate them by querying APIs to build shadow models that mimic the original. As LLMs become more prevalent, safeguarding their confidentiality and integrity is crucial.
As an example, an attacker could exploit a misconfiguration in a company’s network security settings, gaining access to their LLM model repository. Once inside, the attacker could exfiltrate the proprietary model and use it to build a competing service. Alternatively, an insider may leak model artifacts, allowing adversaries to launch gray box adversarial attacks or fine-tune their own models with stolen data.
How to prevent Model Theft:
- Access Controls and Authentication: Use Role-Based Access Control (RBAC) and enforce strong authentication mechanisms to limit unauthorized access to LLM repositories and training environments. Adhere to the principle of least privilege for all user accounts.
- Supplier and Dependency Management: Monitor and verify the security of suppliers and dependencies to reduce the risk of supply chain attacks, ensuring that third-party components are secure.
- Centralized Model Inventory: Maintain a central ML Model Registry with access controls, logging, and authentication for all production models. This can aid in governance, compliance, and prompt detection of unauthorized activities.
- Network Restrictions: Limit LLM access to internal services, APIs, and network resources. This reduces the attack surface for side-channel attacks or unauthorized model access.
- Continuous Monitoring and Logging: Regularly monitor access logs for unusual activity and promptly address any unauthorized access. Automated governance workflows can also help streamline access and deployment controls.
- Adversarial Robustness: Implement adversarial robustness training to help detect extraction queries and defend against side-channel attacks. Rate-limit API calls to further protect against data exfiltration.
- Watermarking Techniques: Embed unique watermarks within the model to track unauthorized copies or detect theft during the model’s lifecycle.
Wrapping it all up
As LLMs continue to grow in capability and integration across industries, their security risks must be managed with the same vigilance as any other critical system. From Prompt Injection to Model Theft, the vulnerabilities outlined in the OWASP Top 10 for LLMs highlight the unique challenges posed by these models, particularly when they are granted excessive agency or have access to sensitive data. Addressing these risks requires a multifaceted approach involving strict access controls, robust validation processes, continuous monitoring, and proactive governance.
For technical leadership, this means ensuring that development and operational teams implement best practices across the LLM lifecycle starting from securing training data to ensuring safe interaction between LLMs and external systems through plugins and APIs. Prioritizing security frameworks such as the OWASP ASVS, adopting MLOps best practices, and maintaining vigilance over supply chains and insider threats are key steps to safeguarding LLM deployments. Ultimately, strong leadership that emphasizes security-first practices will protect both intellectual property and organizational integrity, while fostering trust in the use of AI technologies.