How to Avoid OWASP Vulnerabilities in LLMs
Security frameworks have a reputation for being something you read about after something goes wrong. The OWASP Top 10 for Large Language Model Applications is worth reading before [source].
Released in its 2025 edition by the Open Worldwide Application Security Project, a community of more than 600 security experts across 18 countries, it's the closest thing the industry has to a consensus view on where LLM deployments actually break down in practice. These are real vulnerabilities that security researchers have watched emerge as organizations move AI workloads into production.
What makes this list particularly instructive is what it reveals about the nature of LLM risk. Most of the vulnerabilities on it can't be solved by a single control. Rather, they emerge from the interaction between a model, the data it processes, the infrastructure it runs on, and the applications built around it. Addressing them properly means considering the full deployment stack rather than the model in isolation.
Top 10 Ways to Avoid OWASP Vulnerabilities in LLMS
Here's what each vulnerability actually means, and what it takes to address it.
Prompt Injection
Prompt injection is a sneaky method that could involve embedding malicious commands within a document or database record that trick an AI model's RAG pipeline into treating them as legitimate. It essentially turns the model's own data access against itself, which can be as dangerous as it sounds.
To fight this, you need to implement input validation, output filtering, and strict controls on the model's capabilities. It's also crucial to limit what can be accessed within the execution environment itself; by isolating the model's hardware during runtime, you can contain potential damage from a successful injection.
Sensitive Information Disclosure
LLMs trained on internal data run a genuine risk of unintentionally revealing sensitive data in a number of ways, including revealing personally identifiable information (PII) in answers or confidential business logic that’s unearthed in user queries.
You want to ensure that any sensitive inputs and outputs are never left in plaintext outside of a secure environment during inference. This is achieved by keeping data encrypted in memory throughout the inference process so it can’t be exposed to the host operating system or infrastructure.
Supply Chain Vulnerabilities
The supply chain for an LLM application is complex, as it includes the base model, fine-tuning datasets, third-party plugins, open-source libraries, external APIs and runtime data sources. A compromise anywhere in that chain can spread silently into production.
Teams need to treat the AI supply chain security with the same rigor applied to software supply chains: auditing components, verifying data provenance and enforcing integrity checks before deployment. Cryptographic verification of the execution environment before any workload runs adds another crucial layer here. If something in the stack has been tampered with, attestation catches it before the model ever processes a query.
Data and Model Poisoning
Poisoning attacks tend to happen during the data collection or training stages, but the effects can linger across every subsequent deployment. At first glance, a compromised model might appear to be functioning as intended, even while opening hard-to-spot backdoors that hackers can exploit.
Data provenance combined with thorough model validation before deployment and a secure execution environment can prevent unauthorized changes to model weights during runtime, minimizing the risk of poisoning attacks throughout the entire process.
Improper Output Handling
LLM outputs frequently flow directly into other systems, such as databases, code executors, APIs and user interfaces. When those outputs aren't validated before being passed along, the model's response becomes vulnerable.
The fix is conceptually simple: treat LLM outputs as untrusted input to downstream systems and apply the same validation and controls you'd use for any other external data source. The harder part is ensuring that validation happens consistently across every integration point and without gaps that an attacker can route around.
Excessive Agency
As agentic AI systems take one more real-world actions like querying databases, sending communications or calling external APIs, the scope of potential harm from a compromised model expands in proportion to its permissions. Excessive agency means granting more access than the model actually needs to do its job.
Least-privilege principles apply here like they would with traditional cybersecurity. An LLM that only needs to read from a database shouldn't have write access, and one that drafts communications shouldn't be sending them without human review. Rules governing what a model can access and what it can act on should be enforced at the infrastructure level rather than just through application logic.
System Prompt Leakage
System prompts frequently contain sensitive info like behavioral instructions, data access rules and output constraints. When users extract that information through prompt engineering, they have what they need to get around the model's guardrails.
When security enforcement is built into the infrastructure, independent of the model, through access controls, runtime isolation, and hardware-enforced boundaries, leakage gives an attacker far less than it otherwise would.
Vector and Embedding Weaknesses
RAG systems depend on vector databases and embeddings to surface relevant context. Those components can be exploited through unauthorized access or by manipulating the embedding space, causing the model to retrieve incorrect or harmful context.
Access controls for vector stores are often far less mature than those for traditional databases, so you need to pay close attention as RAG becomes more and more standard. Keeping vector stores within the same protected boundary as the model, including the same access controls and encryption posture, shrinks this oft-overlooked attack surface.
Misinformation
LLMs generate confident-sounding text, whether it's accurate or not. Hallucinations (plausible but incorrect outputs) are a well-documented limitation of current models, and in high-stakes use cases like medical, legal or financial applications, the consequences of acting on a hallucination can be significant.
Automation is great, but most organizations still benefit from a human review process for consequential outputs. Deploying LLMs in roles where a wrong answer creates an inconvenience is much different from deploying them where a wrong answer is actually harmful.
Unbounded Consumption
If you don’t have proper resource controls in place, a poorly thought-out request or a flood of malicious queries can really mess things up, either by affecting service quality or driving costs through the roof. When AI services are billed based on token usage, this unbounded consumption becomes both a security risk and a financial headache.
Rate limiting, request throttling, timeout enforcement, and resource caps all help tackle this issue. The underlying principle: AI systems need clear boundaries, and it’s crucial that they’re enforced at the infrastructure level rather than relying solely on policies to keep things in check.
Ten Vulnerabilities. One Core Cause.
Each of these vulnerabilities is different in mechanism, but they share a root cause: security that lives only at the application layer enforced only by software policy is security that can be bypassed.
The organizations that handle OWASP LLM risks most effectively treat infrastructure as part of the defense, isolating workloads at the hardware level, encrypting data throughout its lifecycle (including while it's being processed), verifying the integrity of the execution environment before anything runs, and enforcing strict boundaries on what models can access and do.
That's a more demanding standard than application-layer guardrails alone can provide. It's also the one that holds up when other guardrails don't.
