.jpg)
TL;DR
- Threat hunting is about staying ahead of attackers, not reacting after the fact
- Strong fundamentals in networking, scripting, and operating system internals are essential
- Hands-on labs and certifications like GCIH and GCFA build confidence and structure
- Collaboration with threat intelligence communities sharpens your perspective
- The mindset of an investigator or detective is a natural fit for this work
Cyber threat hunting is not about waiting for an alert. It is about finding the signals others miss. That means digging through logs, recognizing patterns, and piecing together the kind of story that tells you something deeper is happening. It takes instinct, technical skill, and a willingness to keep pulling threads when most people stop.
Before I got into cybersecurity, I was a federal investigator. What drew me to threat hunting was how much it felt like the same work but just in a different environment. Instead of chasing suspects on the street, I was tracking adversaries through log files and malware. The same attention to detail applied. The same need to build a case, follow evidence, and draw conclusions. If you have an investigative mindset or come from a background in law enforcement, intelligence, or even journalism, you will find threat hunting very familiar.
I started in this space at Verizon, working as a dark web threat hunter. Collaboration played a big role. Groups like ISACs and intelligence feeds from CISA helped me connect the dots and understand bigger patterns. That kind of visibility is critical. You cannot hunt well in isolation.
Certifications like GCIH and GCFA gave me structure and a methodology. They helped me take what I was seeing and frame it through the lens of incident response and forensic investigation. But the real growth came in the field. In one case, I uncovered a web shell that attackers had planted on a legitimate site. The site had been compromised and was serving a fake LinkedIn login page for phishing credentials. It was clean on the surface, but a closer look revealed the foothold. We published a blog post about the threat to warn others, and that case reinforced for me that real threat hunting means looking where no one else is looking.
Why Cyber Threat Hunting Matters
Today’s attackers do not always trigger alerts. They often move laterally, live off the land, and blend in with normal network behavior. That is where cyber threat hunters come in. Their job is to find signs of compromise that are not obvious and piece together fragments of activity that others may overlook.
What sets effective threat hunters apart is their ability to turn vague indicators into actionable intelligence. This work strengthens detection rules and helps prioritize defenses. As attacks become more stealthy and targeted, the demand for professionals who can proactively identify malicious behavior continues to grow.
Threat hunting also plays a critical role in shaping how teams respond to new threats. It is not just about finding the bad actor. It is about understanding how they got in, what they accessed, and how to stop the next attempt. Skilled hunters bring clarity to uncertainty, helping organizations move from reactive to prepared.
In short, threat hunting matters because it creates the intelligence others rely on. It is a force multiplier that improves decision-making, hardens defenses, and uncovers the threats that would otherwise go unnoticed.
Foundational Skills and Knowledge
Before you can hunt threats, you need to understand the environment you are protecting. That means getting comfortable with how systems operate, how traffic moves, and how attackers try to hide. These foundational skills form the base layer of every good threat hunter’s toolkit.
Networking and Operating Systems
Start with the basics. You need to know how data moves across the wire. Understanding TCP/IP, subnetting, and protocols like DNS, HTTP, and HTTPS gives you the ability to spot what looks normal versus what looks off. If you cannot read a packet capture or recognize an anomaly in flow data, it is hard to identify lateral movement or data exfiltration.
Equally important is knowing how operating systems behave. Whether it is a Linux box or a Windows workstation, you should be able to tell when something does not belong. That includes recognizing suspicious file paths, odd registry changes, or unexpected process behavior. You do not need to be a system admin, but you should know enough to catch abnormal patterns.
This kind of host-level understanding has been critical in my own work. Being able to identify command line commands, scheduled tasks, or PowerShell usage that looked suspicious often helped me zero in on threats early.
Scripting and Automation
Threat hunting often involves digging through large amounts of data. Knowing how to script can make that manageable. Python is one of the most useful tools in this space. Whether you are parsing logs, correlating data across sources, or writing a quick detection rule, being able to automate repetitive tasks saves time and sharpens your focus.
PowerShell is especially helpful in Windows environments, while Bash does the job on Linux. You should be able to write and understand scripts that help you work more efficiently.
In my own experience, custom scripts were often the key to speeding up investigations. Whether extracting IOCs or pivoting through datasets, being able to shape tools to fit the hunt makes a big difference.
Security Basics
Every threat hunter needs a solid grasp of core security concepts. Encryption, authentication protocols, and access control models are not just academic topics, they show up in real investigations. You need to know how encrypted channels work to spot potential abuse, and how Kerberos or NTLM can be used or misused in enterprise environments.
Understanding how attackers abuse authentication and privilege escalation paths is critical to finding stealthy compromises. Recognizing misconfigurations in RBAC or unusual service account usage is key.
If you are starting out, certifications like CompTIA Security+ or GIAC GSEC can help you build and validate this knowledge. They provide a strong entry point and make sure you have the right foundation before moving into more specialized training.
Key Specializations in Threat Intelligence
Threat hunting does not stand alone. It connects with several disciplines that deepen your ability to track adversaries and understand how they operate. While many threat hunters begin by reviewing logs or endpoint activity, the real impact comes from combining that data with malware research and broader intelligence. Here are three areas every aspiring threat hunter should be familiar with:
Threat Hunting
At its core, threat hunting is about proactively looking for signs of compromise without relying on alerts. This involves forming a hypothesis, searching through logs and telemetry, and identifying behaviors that suggest something is not right.
Hunting often starts with indicators from shared intelligence feeds. But those leads are just the beginning. The real value comes from asking better questions. What else on this host looks suspicious? What other processes kicked off around the same time? What traffic patterns line up with this activity?
Threat hunters are not just responders. They are investigators who follow activity back to its source and anticipate the next move an attacker might take.
Malware Research
Malware analysis can be challenging, but even basic static review reveals a lot. That kind of detail can shift the entire focus of a hunt.
Knowing how malware behaves gives you an edge. Threats often hide in plain sight, using encoding or built-in system tools to avoid detection. When you learn what to look for, things like unusual PowerShell commands or strange persistence mechanisms stand out quickly.
You do not need to be a full-time reverse engineer to gain value from malware research. Just being able to extract strings, read logs from a sandbox, or observe how a payload acts is often enough to uncover its intent.
Intelligence Analysis
Threat hunting without context is like navigating without a compass. Intelligence analysis adds that direction. It means tracking known attacker groups, reading open-source reports, and linking attacker behaviors to what you are seeing inside your environment. Knowing who is targeting your industry, what tools they prefer, and how they operate helps you hunt with focus and purpose.
Critical Skills for a Cyber Threat Hunter
Being a strong threat hunter is not just about knowing tools or checking boxes. It is about developing the instincts and skills to pull together data, spot patterns, and understand attacker behavior. Once you have the foundation, these are the capabilities that take your hunting to the next level.
Data Analysis and Correlation
Threat hunting often means working with large amounts of raw data. Logs from firewalls, endpoints, DNS, and authentication systems all tell parts of the story. The key is learning how to piece those parts together.
Being able to think across data sources and recognize how a single action on a host links to external traffic or user behavior is what separates a basic analyst from a capable hunter.
Malware Analysis Techniques
You should be comfortable looking at a suspicious file or script and asking the right questions. What is it trying to do? Is it obfuscated? Are there strings or commands that reveal intent? Understanding how malware is built and how it behaves under the surface gives you the ability to detect similar techniques in the wild.
Framework Familiarity
Knowing how to map activity to frameworks like MITRE ATT&CK gives structure to your findings. It also helps you communicate clearly with others, especially when working across teams or with leadership.
If you can say that a set of logs shows execution, credential dumping, and lateral movement, and then align that to known TTPs from groups tracked in ATT&CK, your insights become more actionable. It also helps security teams identify coverage gaps in detection or response.
Hunting is not guesswork. It is a structured investigation. Frameworks like ATT&CK give you a language to describe what you are seeing and support better decision-making.
Building the Right Certifications
Below are some certifications that stand out at different stages of the threat hunting journey:
Entry Level
CompTIA Security+: Security+ is a solid entry point if you are coming from a general IT background or just starting out in cybersecurity. It covers core topics like network security, encryption, access control, and common threat types. While it does not go deep into threat hunting or incident response, it gives you the fundamentals needed to build on.
GIAC Security Essentials (GSEC): GSEC takes it further by providing more hands-on exposure. You will work across Windows and Linux systems, get familiar with threat detection basics, and start seeing how adversaries move through environments. This is a great option if you want a more technical and applied learning path early on.
For those who are new to the field, these certifications help build a shared language, sharpen instincts, and give you the confidence to dive into real investigations.
Intermediate
CompTIA Cybersecurity Analyst (CySA+): CySA+ focuses on behavior-based detection, threat hunting, and incident response. It is well suited for security analysts who have some experience in a SOC or blue team and are ready to take on more proactive responsibilities. It aligns closely with the day-to-day tasks of a developing threat hunter.
GIAC Certified Incident Handler (GCIH): GCIH was a turning point in my career. It showed me how attacks unfold, what to look for at each stage, and how to think like an adversary. It covers everything from initial access to persistence and lateral movement, and how to detect and contain those actions in real time.
This certification helped me tie together theory and practice. It also gave me the framework to communicate findings clearly with technical and non-technical stakeholders alike.
Advanced
GIAC Reverse Engineering Malware (GREM): GREM is for those who want to analyze malware at a deeper level. It teaches how to deconstruct executables, uncover payload behavior, and identify obfuscation techniques. Even if you do not reverse malware daily, having this skill set helps you recognize indicators and support advanced hunting efforts.
Understanding what a piece of malware is doing under the hood gives you insight into attacker intent and helps you develop more targeted detection.
GIAC Network Forensic Analyst (GNFA): GNFA centers on packet-level analysis, network reconstruction, and making sense of raw traffic. This becomes essential when endpoint evidence is unavailable or compromised. It is especially useful for identifying lateral movement, spotting data exfiltration, and confirming attacker activity across systems.
Being able to go back through the wire and tell the story of what happened is a skill that strengthens any investigation.
Hands-On Practice and Self-Study
Certifications help you build knowledge, but hands-on practice is where that knowledge turns into skill. Whether you are setting up a home lab, analyzing live malware, or tackling CTF challenges, this kind of self-directed work is what sharpens your instincts and builds real confidence.
Building a Personal Lab: Documenting your process of how you investigated, what you found, and what tools helped creates a personal playbook you can use later in real investigations.
Capture the Flag (CTF) Events: CTFs are more than competitions. They are realistic exercises that test your ability to think under pressure, solve technical problems, and apply your knowledge. Many include challenges around log analysis, malware decoding, memory forensics, and packet capture review.
I have learned a lot from CTFs, especially when it comes to recognizing potentially malicious behaviors and applying tools quickly.
If you are looking for free or low-cost ways to build experience, CTFs are a great place to start.
Analyzing Known Threat Campaigns: Dig into public threat reports. Study how attackers got in, what techniques they used, and how defenders responded. Then try to replicate the behavior in your lab. Track indicators, map actions to frameworks like MITRE ATT&CK, and practice identifying those behaviors across sample logs or packet captures.
You will start to develop a methodology looking for known patterns, then asking deeper questions about what you see. This kind of work builds the investigative mindset that every good threat hunter needs.
Conclusion
Cyber threat hunting is not for people who wait around for alerts. It is for those who want to investigate, ask better questions, and uncover what others overlook. If you have an investigative mindset, a desire to understand how attackers think, and a willingness to keep learning, this field will challenge and reward you.
What made the difference in my own journey was combining the fundamentals of networking, system knowledge, and scripting with hands-on experience. I spent time in the lab, pulled apart real malware, and followed adversaries across logs and traffic until the story made sense. I also leaned on certifications like GCIH and GCFA to give structure to what I was seeing in the field.
If you are serious about stepping into this role, start now. Build a lab. Dive into technical threat reports and blogs. Sharpen your analysis skills. And join a platform like Cybrary, where you will get access to expert-led courses, real-world labs, and a community of professionals who are working through the same challenges. The adversaries are always evolving and you should be too.
Start learning with Cybrary now and begin your path toward becoming a skilled cyber threat hunter!
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.
