The rise of Large Language Models (LLMs) has opened up endless possibilities in technology. But with great power comes great responsibility or, in this case, great security risks. To ensure safer integration and usage of LLMs, OWASP has released an updated version of its guidence, Top 10 for LLM Applications 2025. This helps in identifying critical vulnerabilities and mitigation strategies. Whether you're a developer, data scientist, or tech enthusiast, understanding these risks is crucial. Let’s break it down.
1. Prompt Injection
Ever tried tricking a chatbot to "tell you its secrets"? That’s prompt injection—a vulnerability where attackers manipulate the input to alter an LLM’s behavior. I'ts like social engineering the model. The root of the problem? LLMs interpret prompts too literally, even when embedded in benign inputs like documents or images.
Practical Tip: Treat your LLM like a mischievous genius. Always sanitise inputs and outputs, set strict boundaries in the system prompts, and validate outputs for anomalies.
2. Sensitive Information Disclosure
Remember when someone left sensitive credentials in a comment during a code review? Now imagine an LLM regurgitating sensitive internal data during a user interaction. This can range from user PII (Personally Identifiable Information) to proprietary algorithms.
How to Mitigate:
Use differential privacy techniques.
Scrub sensitive data from training sets.
Inform users about what not to input. A good analogy? You wouldn’t share your ATM PIN with a bank teller!
3. Supply Chain Vulnerabilities
LLMs often depend on third-party models, libraries, and datasets, which can introduce risks. A poisoned dataset or a tampered pre-trained model can become a silent saboteur in your LLM application.
Best Practices:
Maintain a Software Bill of Materials (SBOM).
Rigorously vet suppliers and third-party libraries.
Adopt strong provenance practices.
4. Data and Model Poisoning
Poisoning an LLM is like spiking a drink. Attackers corrupt training datasets to inject biases or backdoors. The effects can be catastrophic, like an LLM recommending unsafe financial strategies.
Pro Tip: Perform red-teaming exercises and continuously monitor model outputs for anomalies. Think of it as “stress testing” your AI.
5. Improper Output Handling
Improperly handling LLM-generated outputs can lead to XSS attacks, SQL injection, or worse. Imagine an LLM suggesting DROP TABLE USERS; as a query—and your backend executes it without validation.
Practical Tip: Treat LLM outputs as untrusted user inputs. Always validate and sanitise before passing them to downstream systems.
6. Excessive Agency
When LLMs gain autonomy (e.g., executing commands, modifying databases), things can go wrong fast. I once tested an LLM with “admin-like” privileges. A simple typo in a prompt caused a cascade of unexpected actions, locking us out of our system for hours.
Mitigation:
Apply the principle of least privilege.
Use human-in-the-loop mechanisms for high-risk actions.
Keep extensions and permissions minimal.
7. System Prompt Leakage
System prompts, those invisible guiding instructions, often include critical details about how the LLM should behave. If leaked, these can expose internal rules, security configurations, or even API keys.
Example: An fronteir LLM once exposed its system prompt leading to the everyone knowing how it worked.
Fix: Never store sensitive data in system prompts. Think of prompts as “public-facing” scripts.
8. Vector and Embedding Weaknesses
RAG (Retrieval-Augmented Generation) makes LLMs smarter by pulling in external data, but it’s also a double-edged sword. Poorly managed embeddings can leak sensitive information or allow attackers to manipulate responses.
9. Misinformation
LLMs occasionally hallucinate generating plausible-sounding but false outputs. Imagine a legal chatbot “citing” non-existent cases in court. This isn’t just inconvenient; it’s dangerous.
Pro Tip: Implement RAG for verified context and involve human oversight for critical decisions. Never let your LLM have the final say in life-impacting scenarios. Always double check the outputs.
10. Unbounded Consumption
LLMs consume resources like a high-maintenance celebrity. Without limits, they can drain your wallet (Denial of Wallet attacks) or crash your infrastructure (Denial of Service).
What Works:
Apply rate limiting and quotas.
Use anomaly detection for suspicious usage patterns.
Monitor costs dynamically—don’t wait for the bill shock.
Comparison with OWASP Top 10 for LLM Applications 2024
In 2024 and 2025, Prompt Injection remains the top threat to LLM applications, solidifying its position as the most critical concern. Sensitive Information Disclosure has become significantly more urgent, jumping from #6 to #2. Two new vulnerabilities have also emerged for 2025: System Prompt Leakage (#7) and Vector/Embedding Security (#8). Supply chain issues have risen in importance, moving from #5 to #3, now encompassing "Insecure Plugin Design." Additionally, Model Theft has been folded into the broader category of "Unbounded Consumption" (#10), and "Overreliance" has been replaced by "Misinformation" (#9). These updates reflect a growing focus on information security, supply chain integrity, system architecture vulnerabilities, and the unique challenges posed by vector and embedding security.
Why It Matters
Every LLM interaction has potential risks, from exposing sensitive data to spreading misinformation. But with a proactive approach—like following the OWASP guidelines—you can harness their power safely.
Final Thought: Think of securing LLMs as you would childproofing your home. You can’t control their curiosity or creativity, but you can set up guardrails to keep them (and yourself) out of trouble.
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