Large Language Models (LLMs) are transforming the way organizations operate. They automate decisions, assist customers, and power essential systems. However, LLMs are complex and unpredictable, creating bugs that traditional software does not face. If these bugs are not addressed, they can lead to data leaks, unauthorized actions, misinformation, or complete system failure.

This blog highlights the most common types of LLM-specific bugs in 2025 and how to prevent them. Each section explains the bug, its significance, and practical security strategies to fix or reduce it.

Prompt Injection Bugs

Prompt injections occur when the model executes or responds to commands hidden in user input. This is a basic flaw in how LLMs interpret mixed instructions, which can be exploited to override system logic.

Why it matters: Untrusted users can control or influence the model’s behavior, potentially leading to privilege escalation or bypassing important logic.

How to prevent:

• Thoroughly sanitize and filter user input.
• Keep user inputs separate from system prompts using escape boundaries or structured formatting.
• Fine-tune the model to ignore harmful prompts.
• Require human review for high-impact actions like financial transactions.
• Set output limits and restrict model access based on roles and context.

Sensitive Information Disclosure

LLMs can leak private data they were trained on or exposed to, such as personal identifiers, credentials, or internal documents. This can happen either accidentally or through targeted probing.

Why it matters: Leaks can result in GDPR violations, compliance issues, and loss of user trust.

How to prevent:

• Use redaction or anonymization before training or inference.
• Implement strict access controls based on user roles.
• Filter models output using response scrubbing or NLP rules.
• Monitor inputs and outputs for exposure to confidential data.
• Train the model with limits to avoid revealing private content.

Supply Chain Bugs in LLM Ecosystems

Using unverified third-party models, datasets, or APIs can introduce backdoors or altered data into the LLM environment.

Why it matters: An untrusted model or dataset can become a hidden attack vector with significant consequences.

How to prevent:

• Vet all third-party code, datasets, and APIs before use.
• Use secure tracking to verify the source of model components.
• Keep a Software Bill of Materials (SBOM) for visibility on dependencies.
• Enforce strict security principles—do not assume third-party tools are inherently safe.
• Plan for quick replacement of compromised components.

Keep a Software Bill of Materials (SBOM) for visibility on dependencies — tools like sbomapp can help automate this process, making it easier to identify and manage vulnerabilities across your AI supply chain.

Training Data Poisoning

Training data poisoning involves inserting incorrect, biased, or malicious data into the training process, often leading to behavioral changes or deliberate misbehavior.

Why it matters: Poisoned data can subtly alter the model’s responses, leading to harmful, biased, or insecure results.

How to prevent:

• Validate and clean datasets before training.
• Use anomaly detection to identify data inconsistencies.
• Implement strong access controls for training processes.
• Track and audit all versions of training data and model weights.
• Conduct adversarial testing to ensure model resilience.

Improper Output Handling

LLM outputs that are used without processing can lead to injection bugs, misinformation, or unstructured data that disrupts downstream systems.

Why it matters: Un sanitized outputs can create security vulnerabilities or logic errors in the consuming application.

How to prevent:

• Sanitize all model outputs before displaying them in a browser or system.
• Constrain outputs using structured templates or schemas.
• Encode responses properly (e.g., HTML, JSON) to prevent script injection.
• Review high-risk content through a human approval process.
• Limit excessive or repetitive output patterns to detect misuse.

Excessive Agency

LLMs with too much autonomy might take actions without user approval, such as making purchases, deleting data, or accessing protected systems.

Why it matters: Unchecked automation can lead to costly, irreversible actions without human validation.

How to prevent:

• Set strict limits on what the model can do (API restrictions, permissions).
• Require human approval for sensitive operations.
• Use explainability frameworks to validate model recommendations or actions.
• Limit model access based on context, user, or role.
• Monitor model decisions and apply anomaly detection to behavior patterns.

System Prompt Leakage

Bugs that expose internal system prompts or instructions—often through crafted user input—can reveal how the model is organized or controlled.

Why it matters: If attackers understand system logic, they can manipulate it more effectively.

How to prevent:

• Keep system prompts isolated and protected from user-influenced areas.
• Filter out responses that contain leaked meta-prompts or instructions.
• Allow prompt modification only for trusted roles.
• Monitor for input patterns that could expose hidden prompts.
• Apply prompt encryption and privacy methods where applicable.

Embedding and Vector-Based Weaknesses

Vector similarity models may unintentionally reveal information about training data or semantic relationships.

Why it matters: Even without text, attackers can infer sensitive relationships from vectors alone.

How to prevent:

• Audit and clean embedding data to remove sensitive elements.
• Limit user access to embeddings or vector search APIs.
• Conduct adversarial testing on embedding queries.
• Use differential privacy or noise techniques to protect data inference.
• Continuously monitor for unusual access or probing patterns.

Misinformation and Overreliance

LLMs can provide confident but incorrect answers, leading users or systems to make poor decisions if they trust the output too much.

Why it matters: Overreliance can lead to serious operational errors, especially in medical, legal, or financial situations.

How to prevent:

• Integrate fact-checking tools or validated data sources into the response process.
• Include confidence scores or indicators of uncertainty in model outputs.
• Regularly audit and retrain to detect and reduce bias.
• Require editorial review for publishing high-risk content.
• Build feedback loops to refine the model based on user input.

Resource Abuse and Denial of Service

LLMs are resource heavy. Attackers can use large prompts or high request volumes to exhaust memory or processing limits.

Why it matters: Resource exhaustion can disrupt services, deny access to legitimate users, or increase cloud costs.

How to prevent:

• Apply strict API rate limits and user quotas.
• Restrict the size of input prompts and response outputs.
• Monitor usage patterns for unusual spikes or anomalies.
• Prioritize requests based on importance or user type.
• Implement session timeouts, memory limits, and garbage collection.

Model Theft and Unauthorized Access

LLM models contain proprietary data, training efforts, and intellectual property. Without proper controls, these models can be stolen or reverse engineered.

Why it matters: Stolen models can be misused, resold, or used to impersonate your product.

How to prevent:

• Secure model APIs with authentication and encryption.
• Obscure model internals and structure.
• Perform inference in secure enclaves or containerized environments.
• Monitor for suspicious or patterned API queries.
• Restrict access to the model repository and logs.

Final Thoughts

LLM bugs are fundamentally different from traditional software vulnerabilities. They stem from the intricate nature of machine learning systems—shaped by data behavior, language understanding, and probabilistic outputs. Addressing these risks effectively requires robust input/output controls, human oversight, secure infrastructure, and a deep understanding of the LLM lifecycle.

By integrating these strategies early in the design and deployment phases, organizations can build AI systems that are not only high-performing but also secure, reliable, and resilient against emerging threats.

Securing LLMs with IARM

IARM is a CREST-accredited pentesting company specializing in advanced security testing, including LLM Penetration Testing. Our team identifies and mitigates risks unique to large language models—such as prompt injection, data leakage, and model misuse—using proven methodologies aligned with the OWASP Top 10 for LLMs and MITRE ATLAS.

We help organizations securely deploy generative AI by providing comprehensive assessments, threat modeling, red teaming, and compliance consulting (ISO 42001:2023).

🔗 Learn more about our LLM Security Services