Why You Should Care About LLM Security
Your company uses AI tools—maybe a chatbot for support, an AI assistant for reporting, or an intelligent system for automating workflows. Everything runs smoothly—until it doesn’t.
One day your AI starts spitting out sensitive data. A hacker tweaks it to reveal internal documents. Customers report weird, off-brand responses. Worse, your entire system slows to a crawl because someone found a way to overload it with crafted inputs. Suddenly AI security isn’t just a tech problem—it’s a business problem.
LLMs are powerful and vulnerable. Research shows that over 60% of AI applications have at least one critical security flaw and 75% of organizations using AI have seen threats like data leakage, remote code execution, and prompt injection attacks. These aren’t just theoretical risks— attackers are exploiting them to get unauthorized access, manipulate outputs and extract sensitive info.
This article breaks down the OWASP Top 10 LLM Vulnerabilities—the biggest security risks to AI powered applications. More importantly we’ll show you how these attacks happen, what they mean for your business and how to defend against them.
Let’s get started.
Key Security Risks in Large Language Models
| # | Vulnerability | Description | Example | Mitigation Strategies |
|---|---|---|---|---|
|
1 |
LLM01: Prompt Injection |
Attackers manipulate prompts to force unintended behavior. |
A chatbot was tricked into leaking confidential data. |
Implement strict input validation and filtering. |
|
2 |
LLM02: Insecure Output Handling |
Poorly managed outputs can expose sensitive data. |
An AI-generated report leaked PII due to improper sanitization. |
Enforce strict output filtering and review mechanisms. |
|
3 |
LLM03: Training Data Poisoning |
Attackers manipulate training data to alter LLM behavior. |
A competitor inserted misleading data into an open-source dataset. |
Secure training environments and validate data sources. |
|
4 |
LLM04: Model Denial of Service (DoS) |
Overloading the model with malicious inputs causes service degradation. |
Attackers spammed an AI service, leading to downtime. |
Implement rate limiting and anomaly detection. |
|
5 |
LLM05: Supply Chain Vulnerabilities |
Compromised dependencies introduce security risks. |
A malicious third-party plugin led to unauthorized access. |
Conduct strict vetting of external components. |
|
6 |
LLM06: Sensitive Information Disclosure |
The model unintentionally reveals confidential data. |
A legal AI assistant exposed privileged case details. |
Use access controls and data redaction techniques. |
|
7 |
LLM07: Insecure Plugin Design |
Poorly designed plugins create security loopholes. |
An AI plugin allowed execution of arbitrary code. |
Limit plugin permissions and conduct security reviews. |
|
8 |
LLM08: Excessive Agency |
LLMs making autonomous decisions without oversight. |
A trading bot executed unauthorized transactions. |
Define strict operational boundaries and human review. |
|
9 |
LLM09: Overreliance on LLM Outputs |
Users blindly trust AI-generated responses. |
A legal AI provided inaccurate contract terms. |
Implement fact-checking and human validation. |
|
10 |
LLM10: Model Theft |
Unauthorized access to proprietary LLMs. |
A competitor stole an AI model from a cloud repository. |
Enforce strong access controls and encryption. |
LLM01: Prompt Injection
Prompt injection occurs when an attacker modifies user prompts to change the model’s behavior. This can result in unauthorised access, data leakage or malicious content generation.
Risks:
Attackers can override system prompts and get the LLM to generate harmful or misleading outputs.
Social engineering risks increase as attackers craft deceptive inputs.
Compromises data security and the integrity of responses from LLM-based applications.
Example: A financial chatbot was manipulated through prompt injection to reveal private account details by disguising a prompt as a legitimate banking request.
Mitigation:
- Validate inputs strictly to detect and filter crafted inputs.
- Restrict LLM functionality to only what is needed.
- Monitor for vulnerabilities and have controls in place for prompt execution.
- Have strong access controls to prevent modification of system prompts.
LLM02: Insecure Output Handling
LLMs generate output based on user input and training data. If output is not filtered, it can expose sensitive info, violate privacy or introduce harm.
Risks:
- Disclosure of private or confidential info.
- Generation of biased, false or malicious content.
- Legal implications if output breaches compliance.
Example: An AI-powered resume scanner exposed personal details from previously analysed resumes due to poor output sanitization.
Mitigation:
- Sanitize output to filter and review LLM output.
- Vet strictly for sensitive data exposure.
- Monitor and review LLM output continuously.
- Use secure coding to prevent insecure output handling. Example: A competitor injected false data into a public dataset used by an AI-powered stock predictor and the model recommended bad investments.
- Audit and track data sources to prevent access to LLM training environments.
- Parameterised input and type and range checks.
- Monitor regularly for data anomalies.
- Be ethical in AI model training.
LLM03: Prompt Injection
Large Language Models (LLMs) respond to user inputs but prompt injection allows attackers to manipulate those inputs to bypass system instructions, steal sensitive data or force the model to produce harmful output.
Why It Matters
- Attackers can bypass content restrictions and make the model generate misleading, offensive or harmful responses.
- Sensitive internal data can be leaked if the AI is tricked into revealing proprietary or confidential info.
- Training data poisoning can be combined with prompt injection to manipulate model behavior over time.
Real-World Example
A financial AI assistant was designed to give general investment advice without predicting stock prices. But a user cleverly reworded their question to trick the model into sharing financial forecasts and got into regulatory trouble.
Meanwhile an LLM’s training data was poisoned with biased financial reports to subtly influence future responses. This allowed attackers to spread misinformation through AI-powered market analysis tools.
How to Prevent It
Separate system instructions from user inputs – Don’t let users rewrite prompt structure.
- Sanitize inputs with NLP filters – Detect and block malicious prompt patterns.
- Limit response scope – Prevent too much output that can lead to unintended disclosures.
- Defend against training data poisoning – Regularly audit and verify datasets used for model updates.
LLM04: Model Denial of Service (DoS)
AI powered applications need a lot of computing power and are therefore susceptible to Denial of Service (DoS) attacks. A well placed attack can flood the system, slow it down or take it offline.
Why It Matters
- A slowdown can break critical business functions, making AI driven apps unreliable.
- Attackers can exploit excessive resource use, and costs go through the roof.
- Users lose trust when an AI system crashes or fails at the worst possible moment.
Real World Example
Before a major concert, scalpers flooded an AI ticketing bot with automated queries. The system crashed under the load and locked out legitimate buyers causing a PR disaster.
How to Prevent It
- Cap resource usage so a single request can’t consume too much power.
- Apply strict privilege controls to limit who can make high frequency queries.
- Monitor for traffic spikes – early detection can stop an attack before it gets out of handLLM05: Supply Chain Vulnerabilities.
AI models don’t just rely on their own code – they depend on third party components, plugins and external datasets. The entire supply chain is an attack surface.
| Risk | Impact |
|---|---|
|
Unvetted third-party integrations |
Could introduce backdoors and security holes |
|
Model theft from repositories |
Attackers can steal and redistribute proprietary AI models |
|
Malicious dependencies |
A single compromised package can infect entire systems |
Case Study: When Open-Source Goes Wrong
An attacker uploaded a useful looking plugin to an LLM marketplace. Companies quickly integrated it into their AI powered customer service bots but the plugin had hidden malware and caused data breaches across multiple organizations.
How to Reduce the Risk
- Vet third party integrations before adoption.
- Track and log all changes to detect suspicious activity.
- Apply security best practices to minimize risks from external components.
LLM05: Supply Chain Vulnerabilities
AI models are comprised of third-party components—plugins, pre-trained datasets, and external APIs. If any part of this supply chain is broken, attackers can inject vulnerabilities throughout the system.
Why It Matters
- Malicious dependencies—One compromised library can put backdoors in your AI.
- Training data poisoning—Attackers can manipulate public data to change how the AI responds to queries.
- LLM model theft—If repositories or storage environments aren’t secured, attackers can steal your proprietary AI models.
Real-World Example
An open-source plugin was uploaded to an AI marketplace and was designed to siphon off data. Companies that integrated the plugin had no idea their customer interactions were being exposed and had huge data breaches.
In another case, a model trained on external financial news sources was compromised when attackers injected fake articles, slightly biasing the model’s risk assessment recommendations.
How to Prevent It
- Vet all third-party integrations—Only use trusted sources and do security reviews before deploy.
- Log and monitor access—Who is accessing model repositories and external APIs?
- Defend against training data poisoning—Use data validation to prevent manipulated inputs from affecting AI outputs.
- Enforce strict permissions—Restrict access to model storage so nobody can modify it.
LLM06: Sensitive Information Disclosure
Large Language Models are like sponges – they absorb a lot of data during training. But sometimes they leak what they’ve learned.
Why This is Bad
- They might expose personal information from previous interactions.
- Legal consequences from mis-handled user data.* Corporate secrets could be accidentally exposed.
Real World Example
A healthcare chatbot was meant to answer general medical questions but when a user asked the right questions, it revealed snippets of past patient interactions. That’s a HIPAA disaster waiting to happen.
To learn more about HIPAA test, read our post.
What You Can Do
- Limit access permissions – users should only retrieve data relevant to them.
- Avoid broad functionality that allows users to probe the model’s memory.
- Train developers in AI security – mistakes often come from bad implementation.
LLM07: Insecure Plugin Design
Third party plugins can extend the capabilities of AI powered applications but without proper security checks they can also introduce security vulnerabilities.
Imagine, Your company installs a plugin to boost AI productivity. It looks great – until someone discovers it allows attackers to run malicious code on users’ devices. Now sensitive corporate data is leaking and IT is scrambling to contain the damage.
How to Protect Your System
- Audit and verify plugins before they get into your ecosystem.
- Limit plugin permissions – don’t let them access more than they need.
- Monitor for suspicious activity and unusual behavior.
LLM08: Excessive Agency
AI agents can be too independent for their own good. When given too much autonomy they may execute harmful actions, make bad decisions or cause financial losses.
The Cost of Overconfidence
A financial firm deployed an AI powered trading bot to optimize stock trades. It worked – until a flaw in its input validation caused it to misread market signals. Before anyone noticed it had executed millions in unauthorized trades and wiped out profits.
How to Keep AI in Check
- Define strict privilege controls – AI shouldn’t have full autonomy.
- Cap resource use so AI can’t make too many transactions.
- Human-in-the-loop for high risk decisions.
LLM09: Overreliance on LLM Outputs
Just because an AI sounds confident doesn’t mean it’s correct. Relying too much on its responses without verification can be a big problem. AI can output outdated or wrong information.
- Overreliance can lead to bad decisions based on false assumptions.
- Legal and financial risks occur when businesses act on flawed AI output.
Example: AI’s Legal Faux Pas
A law firm used an AI to draft contracts. But the model didn’t account for recent changes in the law. So the firm signed an unfavorable agreement, resulting in financial losses.
How to Avoid This
- Verify AI output before acting on it.
- Update training data with trusted sources and current info.
- Educate users—LLMs are tools, not omniscient.
LLM10: Model Theft
AI models are intellectual property. If someone gets unauthorized access, they can steal the model, reverse-engineer it or distribute it illegally.
What’s at Risk?
- A stolen model can disrupt your competitive advantage.
- Unauthorized copies can be used for nefarious or unethical purposes.
- If someone modifies the model, it could introduce security flaws.
For example, an AI company stored its proprietary LLM in a cloud repository—but didn’t password protect it. Hackers got in, stole the model and leaked it online before it was even released. The company lost millions and had to start over.
How to Protect Your Models
- Implement strict access controls—only authorized users should have access.
- Use MFA and encryption to prevent unauthorized downloads.
- Monitor access logs to detect suspicious activity early.
Keep Your AI Locked Down
If you are using AI in your business, keeping your LLM secure isn’t a one time thing—it’s an ongoing war. Attackers are constantly finding new ways to exploit whether through theft of a model (LLM10), insecure plugin design (LLM07), or excessive agency (LLM08). One oversight could lead to data breaches, financial loss or legal trouble.
So how do you stay ahead of threats? By locking down both your AI models and the systems around them.
| Security Measure | Why It's Important | Best Practices |
|---|---|---|
|
Encrypt Model Files & API Responses |
Prevents unauthorized access and model theft. |
🔹 Use AES-256 encryption for stored models. |
|
Limit Access to Training Environments |
Protects training data and prevents repository leaks. |
🔹 Implement Role-Based Access Control (RBAC). |
|
Monitor for Model Theft Attempts |
Detects unauthorized access before models are extracted. |
🔹 Use real-time anomaly detection (e.g., Splunk, ELK Stack). |
|
Enforce Strong Authentication & Privilege Control |
Prevents unauthorized access to AI systems. |
🔹 Use OAuth 2.0 with short-lived tokens. |
|
Monitor Backend Systems for Exploits |
Detects unauthorized execution attempts in AI pipelines. |
🔹 Regularly audit plugins with OWASP ZAP. |
|
Apply OWASP ASVS Security Policies |
Prevents prompt injections, data poisoning, and security breaches. |
🔹 Implement strict input validation with type and range checks. |
|
Deploy AI Security Tools for Real-Time Protection |
Adapts to evolving AI threats and zero-day exploits. |
🔹 Use runtime security platforms (e.g., Microsoft Defender for Cloud). |
Protect Your AI Models
Your AI model is an asset, if it gets into the wrong hands you will lose your competitive advantage—or worse have your technology used against you. Here’s how attackers target your model and what you can do to prevent it:
✅ Encrypt Model Files & API Responses
Why? If an attacker gets unauthorized access unencrypted models can be stolen and misused.
🔹 How attackers exploit this: By intercepting API responses or accessing cloud storage attackers can steal pre-trained models.
🔹 Best practices:
- Use AES-256 encryption for stored models.
- Secure API responses with TLS 1.3 and payload encryption.
✅ Limit Unauthorized Access to Training Environments
Why? Attackers target repositories and cloud storage to get training data (LLM10).
🔹 How attackers exploit this: Weak authentication allows unauthorized access to training pipelines.
🔹 Best practices:
- Use role based access control (RBAC) to limit user privileges.
- Implement multi-factor authentication (MFA) for all access points.
✅ Monitor for Model Theft Attempts
Why? Unauthorized access logs and unusual API requests may indicate model extraction attempt.
🔹 How attackers exploit this: Adversaries use API scraping techniques to gradually extract an LLM’s parameters.
🔹 Best practices:
- Implement real time anomaly detection with tools like Splunk or ELK Stack.
- Enforce API rate limits to prevent large scale extraction attempts.
Secure Your LLM Applications
For businesses using DevOps as a Service and Cloud as a Service, security goes beyond just the model. It’s about securing the entire AI system from backend exploits, API vulnerabilities and insecure plugins.
✅ Enforce Strong Authentication & Privilege Control
Why? Unauthorized access (LLM01) is the leading cause of AI system breaches.
🔹 How attackers exploit this:
Weak API keys, poor password policies or misconfigured OAuth tokens can allow attackers to get into your system.
🔹 Best practices:
- Use OAuth 2.0 with short lived access tokens.
- Require MFA for API requests from sensitive applications.
✅ Monitor Backend Systems for Exploits & Unusual Activity
Why? Attackers use shadow models and crafted inputs (LLM07, LLM08) to get unauthorized execution access.
🔹 How attackers exploit this: They deploy malicious plugins or exploit unfiltered user inputs to manipulate model outputs.
🔹 Best practices:
- Regularly audit LLM plugin security with tools like OWASP ZAP.
- Log all API interactions and use SIEM solutions like Splunk to detect anomalies.
✅ Apply OWASP ASVS Security Policies
Why? Unvalidated user input (LLM03) can lead to prompt injections, data poisoning or security breaches.
🔹 How attackers exploit this: Poorly sanitized inputs allow attackers to override model instructions, extract private data or inject false responses.
🔹 Best practices:
Implement strict input validation with type and range checks.
Sanitize user prompts with regular expressions and NLP filters.
✅ Deploy AI Security Tools for Real-Time Protection
Why? AI models evolve fast, but security must evolve faster.
🔹 How attackers exploit this: They use zero day vulnerabilities in LLMs before developers can patch them.
🔹 Best practices:
- Use runtime security platforms like Microsoft Defender for Cloud to detect LLM-based threats.
- Continuously monitor LLM outputs for bias, hallucinations or unexpected behavior.
LLM Security is Not “Set It and Forget It”
Every AI system is a moving target, and attackers will always find new ways in. To secure your LLM you need constant watchfulness, smart restrictions and proactive threat detection. By following these best practices you can reduce risks while unleashing the power of your AI applications.
Conclusion
Large Language Model applications are changing industries but their security can’t be ignored. Attackers can inject direct prompts, exploit insecure input parameters and remote code execution to manipulate outputs, steal data or get unlimited access to a system. These risks can lead to data leakage, data exfiltration or full system compromise making web application security top priority.
Securing the model requires more than awareness – it demands action. Businesses must:
- Validate inputs and filter inputs to prevent indirect prompts and other critical security vulnerabilities.
- Apply least privilege to block unauthorized access and reduce excess resources available to LLM plugins.
- Monitor for anomalies that may indicate attacker interactions or side channel attacks.
- Follow the Application Security Verification Standard and align security strategy with the Top 10 for LLM.
By adopting a security-first approach organizations can mitigate risks, govern properly and protect AI-driven systems from misinformation, tampered outputs and bad decisions. The right mix of frameworks, methodologies and deployment safeguard will allow businesses to verify their AI – before attackers do.
Top 5 Technical FAQs on LLM Security
-
1. How do I know if my LLM is being attacked?
Attackers try to extract your LLM by sending a bunch of queries to build your LLM. To detect this:
- Monitor API request patterns – flag same source requests.
- Use anomaly detection tools like Splunk, ELK Stack, Microsoft Defender for Cloud
- Implement query fingerprinting.Track query-response pairs to detect malicious behavior.
- Monitor API request patterns – flag same source requests.
-
2. What are the best tools to monitor and log AI security incidents?
To detect and respond to security threats:
- Splunk or ELK Stack provide centralized log analysis and anomaly detection.
- Prometheus + Grafana allow LLM resource usage tracking for unusual spikes.
- AWS GuardDuty / Azure Security Center offer AI specific cloud threat detection.
- Falco or OpenPolicyAgent (OPA) are used for Real-time security policy enforcement.
- Splunk or ELK Stack provide centralized log analysis and anomaly detection.
-
3. How do I prevent prompt injection attacks?
- Sanitize user inputs. Use regex and NLP filters to block malicious commands.
- Apply context aware filtering. It will strip harmful commands dynamically.
- Isolate user inputs from system prompts to prevent user queries from overriding system logic.
- Limit model responses can grant restrict execution of unintended com
- Sanitize user inputs. Use regex and NLP filters to block malicious commands.
-
4. How do I prevent unauthorized API access to my LLM?
To prevent token interception, utilize OAuth 2.0 with PKCE.
- Short-lived access tokens can help reduce risk in case of breach.
- Mutual TLS (mTLS) authentication ensure only verified clients can talk to the API.
- RBAC + ABAC are used for API permissions based on user roles.
- Short-lived access tokens can help reduce risk in case of breach.
-
5. hat encryption should I use to protect my LLM models?
- AES-256 encryption – Store model files.
- TLS 1.3 with Perfect Forward Secrecy (PFS) – Encrypt data in transit.
- Secure Enclaves (Intel SGX / AWS Nitro) – Isolate AI workloads.
- Homomorphic encryption for computing on encrypted data without decryption.
- AES-256 encryption – Store model files.
Have More Questions? Let’s Talk!
AI security is complex, and every business has unique challenges. If you have questions about securing your AI infrastructure, managing cloud environments, or improving DevOps security, A-Dev is here to help.
Reach out to us for expert guidance, customized solutions, and best practices tailored to your needs. Let’s make your AI-powered systems secure, scalable, and efficient—together.
