TL;DR
- Network security engineers defend data and systems by building resilient, secure network architectures.
- Strong fundamentals in networking, firewalls, VPNs, and intrusion detection are essential.
- Real growth happens through hands-on practice, whether in a lab or during real-world engagements.
- Certifications such as CCNA, Network+, and GCIA provide structure and credibility for both entry-level and advanced professionals.
- If you enjoy solving puzzles, thinking systematically, and understanding how systems work (and break), network security engineering might be for you.
- Cybrary offers a collection of courses, hands-on labs, and a supportive community to help you level up.
Picture this: It’s 2:14 a.m., and an alert pops up in the SIEM. An outbound DNS request doesn't match any known behavior. It’s subtle. No one's calling it a breach yet, but something feels off. You know which logs to check, which systems to review, and which tools will give you the clearest picture. Within the hour, you’ve confirmed suspicious activity while containing the host and blocking further communication. What could have turned into a full-blown breach is neutralized before it spreads thanks to your quick thinking.
That’s the value a network security engineer brings to the table—and it’s exactly why learning how to become a network security engineer can make you indispensable in moments like this.
This role isn’t just about writing firewall rules or monitoring dashboards. It’s about understanding how systems connect, how attackers move throughout a network, and how to respond quickly when something doesn’t look right. It's about knowing how to reduce risk before it turns into damage.
My own journey started with teaching networking and dark web courses, but it was during penetration testing where I truly grasped how attackers exploit weak network infrastructure. Those experiences taught me to think like an adversary. That mindset now informs how I defend networks, guide remediation, and lead security improvements.
If you’re analytical, calm under pressure, and motivated by solving real problems, network security engineering is a career where you can thrive. In this post, I’ll walk you through the core skills, certifications, tools, and resources, including how platforms like Cybrary can help you gain hands-on experience and become the person who stops the breach before it happens.
Why Network Security Engineering Matters
Network security engineers play a vital role in protecting critical assets. They ensure that sensitive data, intellectual property, and key infrastructure remain secure from unauthorized access and external threats. Whether it's a healthcare provider safeguarding patient records or a financial institution protecting transaction data, these engineers act as the front line of defense for enterprise networks.
The demand for skilled professionals in this space is growing rapidly. With the constant evolution of threats, from ransomware to advanced persistent threats, organizations are actively seeking experts who understand how to secure complex, hybrid environments. According to Talent.com, the average salary for a network security engineer in the U.S. is approximately $125,000 per year, with entry-level positions starting at around $104,645 and experienced professionals earning up to $164,556 annually.
Engineers who can architect resilient systems, enforce secure access, and monitor for anomalies are indispensable to a strong security posture.
A widely publicized example is the 2021 Colonial Pipeline ransomware attack. Threat actors gained access through a compromised VPN account that lacked multi-factor authentication. This single point of failure forced one of the largest fuel pipelines in the U.S. to shut down operations, causing widespread disruption. While the breach began with credential misuse, poor segmentation and limited monitoring enabled the attackers to move quickly within the environment. The incident highlighted the urgent need for layered, proactive network security.
Knowing how to become a network security engineer offers more than technical growth. It opens doors to in-demand job roles, competitive salaries, and long-term career advancement. Whether you're aspiring to be a senior engineer, architect, or even a CISO, mastering network security fundamentals lays the groundwork for future leadership.
Cybrary supports this journey by providing structured learning paths tailored to real-world job roles. Their labs, certification prep, and expert-led content ensure you build the confidence and competence to excel in modern network security environments.
Essential Skill Sets
Networking Fundamentals
Everything in cybersecurity begins with understanding the flow of data. TCP/IP, DNS, DHCP, routing, and switching aren’t just concepts, they’re the terrain adversaries move across. Learning to read a packet capture or trace a route through a segmented network is foundational to detecting abnormal behavior.
When I was a penetration tester, the most effective pivot points I used were often network misconfigurations. Understanding protocols like ARP and DNS allowed me to tunnel traffic in ways defenders didn’t anticipate.
Cybrary’s Network Sniffing Basics lab gives hands-on experience to learn the basics of network sniffing, including passive and active sniffing techniques. It’s a great starting point for developing the instincts you’ll need in the field.
Network Architecture and Design
Defensive design matters. Concepts like defense in depth, zero trust architecture, micro-segmentation, and network access control are essential for limiting attacker movement.
I’ve worked in environments where a flat network could turn a small breach into a full compromise. The goal is to build networks with access tiers, separate trust zones, and identity-aware firewalls. This results in more control with better visibility and stronger containment.
Understanding modern architectures like Software-Defined Networking (SDN) and Secure Access Service Edge (SASE) also puts you ahead of the curve. These enable dynamic control policies that are easier to manage and enforce at scale.
Troubleshooting and Analysis
Network security engineers are often called in during moments of confusion. Identifying irregularities in network traffic and spotting subtle anomalies are core to the job. These could include unusual spikes in outbound traffic, irregular DNS query patterns, or internal systems making unexpected external connections. The ability to recognize these signs early can prevent an attack from escalating.
Once a potential threat is identified, the next step is analyzing it. Packet capture tools like Wireshark and Zeek allow engineers to dissect network activity in real time or retroactively. These tools help uncover the root causes of issues, reveal indicators of compromise, and provide visibility when other logs fall short. Using these tools effectively requires both practice and intuition, skills that develop through repetition and hands-on learning.
Cybrary’s Wireshark Basics course helps build this muscle with hands-on packet capture analysis and real-world walkthroughs.
Automation and Scripting
Automation plays a major role in helping network security engineers manage repetitive tasks, parse large data sets, and maintain consistency across environments. Python, PowerShell, and Bash are especially valuable for writing scripts that streamline log analysis, system checks, threat detection, and response workflows.
Many network security engineers use scripting to solve problems. For example, Python is often used with regex to parse firewall logs, isolate suspicious domains, and cross-reference findings against threat intelligence feeds. These kinds of custom automations can trigger real-time alerts through platforms like Slack, giving teams faster insight and earlier response windows.
In larger environments, tools like Terraform and Ansible are used to manage infrastructure in a more consistent and scalable way. They help automate network configurations, enforce baseline security controls, and ensure that systems can be deployed reliably across different environments, especially in cloud-first or hybrid networks.
If you’re just starting with scripting, Cybrary’s Python for Cybersecurity Professionals is a great place to begin applying it directly to cybersecurity use cases.
Certifications for Aspiring Network Security Engineers
Cisco Certifications
CCNA Security or CCNP Security
These certifications build your ability to design and secure enterprise networks, especially in Cisco-heavy environments. They cover essential topics such as access control lists, VPNs, intrusion prevention, and secure routing. If you're working with Cisco infrastructure, these certs validate real-world, hands-on skills in configuring secure network solutions.
CompTIA Certifications
Network+ and Security+
Network+ provides foundational knowledge of network architecture, protocols, and troubleshooting. Security+ broadens your understanding to include threat detection, risk management, and incident response. These are excellent entry-level certifications for those just beginning their journey into network security.
GIAC Certifications
GSEC and GCIA
GSEC is ideal for professionals looking to prove hands-on security knowledge across multiple operating systems and real-world scenarios. GCIA focuses on network intrusion analysis, log interpretation, and packet-level investigation, which are key skills for network security engineers.
Resources and Practical Learning
Virtual Labs and Home Lab Setups
Experience is the ultimate teacher. One of the best ways to build practical knowledge is by creating a home lab that mirrors real-world network setups. Using virtualization platforms like VirtualBox or VMware, you can configure systems, experiment with traffic flows, and safely troubleshoot misconfigurations in your own environment.
Tools like pfSense, Suricata, and Snort give you the chance to get hands-on with technologies that are widely used in production environments. You’ll gain familiarity not just with how they work, but with how they fail and how to recover when they do.
If you're not ready to build your own environment, Cybrary offers virtual labs that let you dive directly into realistic scenarios. These environments come pre-configured, so you can focus entirely on strengthening your technical skills and decision-making.
Online Courses and Learning Paths
Cybrary offers a flexible collection of courses and hands-on labs that help you build and apply network security skills in realistic environments. These labs give you access to the kinds of tools and scenarios professionals encounter on the job, making it easier to bridge the gap between theory and practical application.
Community and Collaboration
The best security professionals aren’t lone wolves, they’re part of a community. Cybrary’s Discord server (open to Cybrary Insider Pro members) and Forums are full of learners and mentors who share career advice, troubleshooting tips, and give encouragement when things get tough.
Outside of Cybrary, participate in CTFs, join local meetups, and follow blue team conversations on social platforms to stay on top of trends. Consider organizations like InfraGard, (ISC)2, or ISSA, which often hold local workshops and networking events. In my own journey, a single conversation at a meetup opened doors I hadn’t considered. It introduced me to TShark, which I later used to streamline packet analysis tasks directly from the command line.
Conclusion
That 2:14 a.m. incident in the introduction is more than just a story, it’s a reminder of what’s at stake and what this role demands. The engineers who succeed in those moments aren’t lucky, they’re prepared.
If you're wondering how to become a network security engineer, this is where the path begins. It starts with curiosity, the drive to solve problems, and the ability to think critically under pressure. The best engineers are systematic thinkers who understand how systems interact and where weaknesses can emerge. They learn by doing, building their own labs, capturing packets, or writing scripts to solve real-world challenges. They stay humble and hungry, always sharpening their tools, updating their knowledge, and going for new certifications.
That’s where Cybrary helped me and where it can help you. From structured learning paths to labs that mimic real incidents, the platform gives you more than knowledge - it builds instincts. The kind of instincts that help you act quickly to contain threats and protect the people who rely on you.
Sign up for Cybrary to access specialized network security courses, interactive labs, and support from an active cybersecurity community. By continuously upskilling and connecting with mentors, you can accelerate your path to becoming a network security engineer today!
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.