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“My LLM is secure because it’s built by a major vendor.” How often do you say this to yourself?

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LLMs are everywhere now. They are being integrated into business processes, security tools, and customer interactions at breakneck speed. But security isn’t keeping up. Attackers are already exploiting vulnerabilities in ways that traditional security controls weren’t designed to handle. Data leakage, prompt injections, and model manipulation are happening at this very moment to a very real business.

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What I’m trying to say is that if you’re using LLMs, you’re increasing your attack surface. And stop asking if you’ll get attacked. It’s how soon and how bad.

Table of Contents

• Reason #1: Uncontrolled Data Exposure
• Reason #2: Prompt Injection Attacks
• Reason #3: Model Manipulation & Poisoning
• Reason #4: Over-Reliance on AI Without Security Controls
• Reason #5: Compliance & Legal Blind Spots

Reason #1: Uncontrolled Data Exposure

LLMs don’t have any idea about the difference between public data and confidential information (unless you tell them). And if you’re not careful, they can accidentally expose customer PII, trade secrets, or internal business data in their responses.

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This happens because:

• Your LLM was trained on raw and unfiltered data, and it absorbed and regurgitated sensitive details without any limitation.
• Sensitive data is used in model training but because strict policies were not implemented, the data becomes part of the model’s knowledge base.
• Your LLM interacts with external apps and services with unsecured APIs that expose more data than intended.

How to prevent sensitive data leakage

• Classify and mask sensitive data before it ever reaches the model. Don’t let your LLM store or process critical information without securing it first.
• Use retrieval-augmented generation (RAG) instead of fine-tuning using sensitive data. RAG pulls relevant information from a secure source at runtime instead of embedding it directly into the model.
• Run regular audits on your AI interactions. Log and review responses to catch potential leaks before they become security incidents.

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Reason #2: Prompt Injection Attacks

Not all attacks require hacking into a system, sometimes, all it takes is a cleverly worded prompt to make an LLM spill sensitive data or take unauthorized actions. This is called a prompt injection attack, and if you’re not protecting against it, your AI-powered workflows are compromised.

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This happens because:

• LLMs don’t validate context properly. They take inputs at face value and can be tricked into bypassing rules.
• Your LLM accepts any user prompt without strict controls, which attackers take advantage of to inject malicious instructions.
• Teams blindly trust AI-generated responses. 
• Your LLM is connected to your business systems without strict guidelines. An injection attack here could trigger real-world actions (like modifying databases or sending emails).

How to prevent prompt injection attacks

• Use allowlist-based filtering to block suspicious prompts and prevent the LLM from executing unauthorized instructions.
• Limit what AI-generated actions can be executed based on user roles and permissions.
• Simulate attacks to identify and patch vulnerabilities before attackers can exploit them.

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Reason #3: Model Manipulation & Poisoning

If your LLM is trained on corrupted or manipulated data, it can become a security risk instead of an asset. Attackers can poison training datasets to enable biases, misinformation, or even hidden backdoors that let them exploit the model later. Once the damage is done, it’s nearly impossible to undo.

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This happens because:

• You didn’t verify your data sources, so attackers were able to inject malicious data during training.
• There are no integrity checks in your data pipelines, and your training data isn’t regularly validated.
• Over time, an LLM can shift away from its intended behavior, which makes it easier for attackers to exploit.
• You’re using pre-trained models without auditing them.

How to prevent model manipulation and poisoning

• Verify every data source carefully before using it in training. For external datasets, make sure that you will be extra careful.
• Prevent attackers from injecting harmful data by adding safeguards that protect against tampering.
• Track model behavior to detect drift, biases, or signs of adversarial manipulation before they become a real problem.  

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Reason #4: Over-Reliance on AI Without Security Controls

LLMs are powerful, but they shouldn’t be making unchecked decisions that impact security, compliance, or business operations. Too many organizations assume AI-generated content is always correct, ignoring the risks that come with over-reliance on automation. When security isn’t built into AI deployments, bad decisions will go unnoticed until their impacts are already in front of you.

This happens because:

• People assume AI is always right. LLMs generate very confident responses, even when they’re wrong.
• There’s no human-in-the-loop (HITL) validation. Critical decisions get automated without a human checking for errors or biases.
• Without clear policies, AI is deployed without understanding security risks or compliance implications.
• You integrated third-party AI models without auditing how they handle data and security.

How to prevent over-reliance on AI without security controls

• Align AI usage with your business’s risk tolerance and regulatory requirements.
• Use AI to assist, not replace, human decision-making. Keep humans involved, especially in high-stakes processes.
• Regularly assess your AI-driven workflows to catch risks early.

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Reason #5: Compliance & Legal Blind Spots

Regulators are watching how enterprises use AI, and if your LLM deployment isn’t aligned with laws like GDPR, HIPAA, or industry-specific regulations, then you’d better be ready for fines, lawsuits, and major reputational damage. But who am I kidding? No one is ready for those. Companies that rush to integrate AI typically forget (conveniently) the compliance risks that come with it. 

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This happens because:

• Many businesses don’t have a solid understanding of how existing laws apply to LLMs.
• AI-generated content is usually stored without clear policies, creating privacy risks.
• If your AI system makes a decision that impacts customers or operations, there’s often no audit trail explaining why.
• You did not consult compliance experts before deploying AI.

How to prevent compliance & legal blind spots

• Treat AI like any other system that handles sensitive data.
• Maintain detailed records of AI-generated outputs to meet audit and regulatory requirements.
• Work with legal teams from the start to establish clear guidelines for how AI can be used while staying compliant with regulations.

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LLM security needs to be built in

You're deploying LLMs at scale, but are you securing them properly? If your answer is not YES, then your AI might already be leaking sensitive data, getting manipulated by attackers, or making high-risk decisions without you knowing.

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This is a business risk, and it’s a huge one. Regulators might already be on you, or worse, it could be the attackers already exploiting the gaps in your security. And the only way to keep these from happening is to treat LLM security as an important business priority that it is.

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If you don’t have a clear and tested strategy for securing LLMs, now is the time to build one. Join us for a deep dive into AI security with real-world solutions that work with Abhay Bhargav.

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On February 12, 2025, at 9 AM PST, AppSecEngineer will host LLM Secure Coding – The Unexplored Frontier. We’ll cover secure coding strategies for LLMs, the top GenAI threats of 2025, and how to future-proof your AI security.

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Register now. Because LLM security, as AI has said over and over again, is not optional.

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Frequently asked questions

What are the biggest security risks of using LLMs in enterprises?

The top security risks include uncontrolled data exposure, prompt injection attacks, model manipulation, over-reliance on AI without security controls, and compliance blind spots. These can lead to data leaks, unauthorized AI behavior, regulatory violations, and financial losses.

How can prompt injection attacks compromise an LLM?

A prompt injection attack manipulates an LLM by inserting malicious instructions that trick the model into revealing sensitive data, bypassing restrictions, or executing unauthorized actions. This happens because LLMs lack strong context validation and trust user inputs too easily.

How do you prevent data leaks in AI models?

Preventing LLM data leaks requires strict data classification, masking sensitive information, and limiting what data is used in model training. Using retrieval-augmented generation (RAG) instead of embedding sensitive data directly into models can also help reduce risks.

What is model poisoning, and how does it affect AI security?

Model poisoning is when attackers manipulate training data to embed biases, misinformation, or backdoors into an AI model. Once poisoned, an LLM can produce incorrect, unethical, or exploitable outputs that compromise business operations and decision-making.

What security controls should enterprises implement for LLMs?

Key security controls include:

• Input validation and filtering to block malicious prompts
• AI governance frameworks to ensure compliance with regulations
• Adversarial testing to simulate attacks and identify vulnerabilities
• Continuous monitoring to detect anomalies in AI behavior

How do compliance regulations like GDPR and HIPAA apply to LLMs?

GDPR, HIPAA, and similar regulations govern how enterprises handle sensitive data, and that includes AI models. If an LLM processes PII, healthcare records, or financial data, organizations must ensure proper data governance, consent management, and auditability to avoid legal penalties.

Can LLMs make decisions without human oversight?

LLMs should not make critical decisions without human-in-the-loop validation. AI-generated outputs can be biased, incorrect, or security risks, so enterprises should implement AI-assisted decision-making rather than full automation for high-risk processes.

How do you secure third-party AI models and APIs?

To secure third-party AI tools, enterprises should:

• Audit the model’s data sources and security policies
• Use API access controls to restrict data exposure
• Monitor AI-generated outputs for compliance and security risks

What are the best practices for securing AI-generated content?

• Implement logging and explainability tools to track AI decisions
• Use differential privacy to prevent data leakage
• Conduct periodic security reviews to catch vulnerabilities early

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