As organisations continue to integrate Large Language Models (LLMs) and Generative AI (Gen AI) applications into their operations, the security and ethical considerations surrounding these technologies have never been more urgent. The Open Worldwide Application Security Project (OWASP) has outlined the top 10 most pressing risks associated with Gen AI in their 2025 report-offering a critical roadmap for any enterprise aiming to deploy AI responsibly and securely.

At GAEA AI, where trust, truth, and transparency form the bedrock of our Large Geotemporal Model (LGM), we are committed to helping organisations mitigate these evolving threats. Below, we summarise OWASP’s Top 10 risks and explore key mitigations to keep your AI systems safe, accountable, and effective.

1. Prompt Injection:

The Risk:
Maliciously crafted user inputs manipulate the LLM’s behavior, leading to unintended outputs or actions. These “invisible” prompts can bypass human scrutiny while still being processed by the model.

Mitigation:
Implement robust input validation and sanitization techniques. Employ contextual awareness within the LLM application to detect and neutralize adversarial prompts. Consider using prompt engineering best practices to guide model behavior and limit its flexibility in responding to unexpected inputs.

2. Sensitive Information Disclosure:

The Risk:
LLMs inadvertently expose sensitive data, including PII, financial details, confidential business information, and even proprietary model details. This can occur through training data, model outputs, or interactions with connected systems.

Mitigation:
Implement strict data governance policies, including anonymization and pseudonymization techniques for training data. Carefully control the information accessible to the LLM and its applications. Employ output filtering and masking to prevent the leakage of sensitive details.

3. Supply Chain Vulnerabilities:

The Risk:
Weaknesses in the LLM supply chain, encompassing training data sources, pre-trained models, and deployment platforms, can introduce biases, security breaches, or system failures. Reliance on third-party components expands the attack surface.

Mitigation:
Thoroughly vet and monitor all components of the LLM supply chain. Implement processes for verifying the integrity and security of training data and pre-trained models. Establish secure deployment pipelines and regularly audit dependencies.

4. Data and Model Poisoning:

The Risk:
Malicious manipulation of training or embedding data introduces vulnerabilities, backdoors, or biases into the model. This can lead to degraded performance, generation of harmful content, or exploitation of downstream systems.

Mitigation:
Implement robust data validation and cleaning processes. Employ anomaly detection techniques to identify and mitigate data poisoning attempts. Continuously monitor model performance and outputs for signs of compromise. Consider techniques like federated learning with secure aggregation to enhance data security.

5. Improper Output Handling:

The Risk:
Failure to adequately validate, sanitize, and handle LLM-generated outputs before they are used by other systems can create security vulnerabilities. This is akin to providing indirect access to functionalities based on potentially malicious prompts.

Mitigation:
Treat LLM outputs as untrusted data. Implement rigorous validation and sanitization processes before integrating them into downstream systems. Apply context-aware filtering and encoding to prevent unintended actions or information leakage.

6. Excessive Agency:

The Risk:
Granting LLM-based systems excessive autonomy to call functions or interact with other systems (via plugins or tools) can lead to unintended or malicious actions, especially if the LLM’s decision-making process is compromised.

Mitigation:
Implement the principle of least privilege for LLM agents. Carefully define and restrict the scope of actions they can perform. Implement robust authorization and auditing mechanisms for all external interactions initiated by the LLM. Incorporate human review and approval workflows for critical actions.

7. System Prompt Leakage:

The Risk:
System prompts, which guide the LLM’s behavior, may inadvertently contain sensitive information. If these prompts are leaked, attackers can gain insights into the application’s inner workings and potentially bypass security controls.

Mitigation:
Avoid including sensitive information directly in system prompts. Store sensitive configurations securely and reference them indirectly. Implement mechanisms to prevent the LLM from revealing the system prompt. Regularly review and audit system prompts for potential information leakage.

8. Vector and Embedding Weaknesses:

The Risk:
Vulnerabilities in the generation, storage, or retrieval of vectors and embeddings in Retrieval Augmented Generation (RAG) systems can be exploited to inject harmful content, manipulate model outputs, or gain access to sensitive information within the knowledge base.

Mitigation:
Employ robust techniques for generating and storing embeddings, including anomaly detection and access controls. Implement secure retrieval mechanisms and validate the integrity of retrieved content. Consider using encryption for sensitive vector data.

9. Misinformation:

The Risk:
LLMs can generate false or misleading information that appears credible, leading to security breaches, reputational damage, and legal liabilities for applications relying on their output.

Mitigation:
Implement mechanisms to verify the accuracy and reliability of LLM-generated information. Employ techniques like fact-checking against trusted sources and providing citations. Clearly communicate the potential for inaccuracies to users.

10. Unbounded Consumption:

The Risk:
Uncontrolled generation of outputs by LLMs can lead to excessive resource consumption, denial-of-service attacks, and unexpected costs.

Mitigation:
Implement rate limiting and resource quotas for LLM inference. Monitor resource usage and establish thresholds to prevent abuse. Optimize prompt design and model parameters to improve efficiency.

Moving Forward: Responsible AI Adoption

Generative AI is accelerating faster than most industries can keep up with. But innovation without security is a liability. By understanding and addressing these risks proactively, businesses can unlock the immense potential of Gen AI, safely and responsibly.

At GAEA AI, we believe in designing for resilience, security, and truth from the ground up. Our Large Geotemporal Model (LGM) AI ensures not just powerful outputs, but trusted ones.