There are LLMs everywhere now. Chatbots, writing tools, customer support, and pretty much everywhere on the web. With great power comes great responsibility. As we prepare for the end of the year 2025, the only true security for LLMs will be protecting them from manipulation, hacking, or unforeseen behaviour. Each day, more companies use AI and machine learning for business operations, so now it is important to outline how organisations should secure their systems from bad actors. This paper offers an overview of LLM security, why you should care about it, and how you can begin to explore what it means to secure LLMs.
What is LLM Security?
The focus of LLM Security is protecting large language models against risk, abuse, or malicious behaviours. This involves safeguarding their inputs, outputs, training data, and finally the LLM’s environment. As LLMs are becoming integrated into business workflows, we will need security around everything they impact, whether that is data privacy or API access. In short, the LLM Security framework is cybersecurity for AI language models.
Why LLM Security Matters in 2025
Businesses are quickly incorporating LLMs into their workflows. Think healthcare, finance, and marketing, which means that LLMs can now process sensitive data and make decisions. In 2025, threat actors are able to leverage an array of vulnerabilities, such as exposed servers and weak access controls, like over 1100 Ollama LLM servers that were recently exposed to the internet. On top of that, LLM security vulnerabilities can be “jailbroken” and can do malicious things or create malware packaged into images and other formats. Therefore, security is absolutely warranted, not just appreciated.
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Top 10 LLM (Large Language Model) Security Risks (2025)
As LLMs become more powerful and more widely adopted in 2025, attackers are also becoming more sophisticated in their abuses of LLMs. Researchers have already shown us tricks we could use in shaping prompts to cause a large language model to produce unwanted results (referred to as “prompt injection”).
Data leaks are no longer a theoretical or distilled risk—they are happening. Organisations need to understand the vulnerabilities in LLM technologies or adopt LLM security testing to protect their sensitive data, sustain trust, and avoid risk/exposure to loss. Here are ten of the biggest LLM security risks facing organisations in using large language models today.
1. Prompt Injection
Prompt injection occurs when attackers create clever prompt instructions that bypass a model’s safety guidelines. For example, a user query may contain a hidden command that allows an LLM to disclose confidential information or alter restrictions. It is like SQL injection, but for AI prompts, and is one of the most common attack vectors today.
Read Also: How to Perform an AI Risk Assessment
2. Sensitive Information Disclosure
The last example mentioned, if not safeguarded properly, brings to light a problem called Sensitive Information Disclosure. LLMs may “remember” or inadvertently share sensitive information from training data or conversations. This may include disclosing company secrets, personal information, or even customer records. An LLM, potentially compromised, may easily disclose much more than it should, highlighting the importance of large language model security.
3. Supply Chain Vulnerabilities
When using LLMs, many rely on external datasets, APIs, and plugins or pre-trained models. When these external sources are corrupted, attackers may insert malicious code or poisoned data into the LLM. Similar to traditional software supply chain attacks, the weakest link in the supply chain may introduce LLM vulnerabilities to malicious exploitation.
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4. Data & Model Poisoning
Malicious individuals or third parties can tamper with training or fine-tuning data and cause LLM outputs to be manipulated. Poisoned data can result in biased, false, unsafe, or harmful responses. In more serious cases, poor llm data security can lead to poisoned data. It can make the LLM behave unpredictably in production and adversely affect trust.
Explore: Data Security Services in Cybersecurity.
5. Output Mismanagement
LLM outputs may look perfectly harmless, yet there can be risks that are hidden. For example, while unsafe LLM responses may not seem risky, they may be malicious, execute code, leak sensitive data, or allow cross-site scripting. Organisations that trust LLM outputs without caution may become exposed to security incidents.
6. Excess Agency
When coupled with outside systems, such as APIs or automation tools, LLMs will be allowed to act on behalf of their users. Excess agency means that if an attacker or malicious entity or actor could convince the LLM to have it delete files, make purchases, or send unauthorised requests, that would be a risk. In essence, in circumstances where too much agency exists without proper checks and balances, there can be significant risks.
7. System Prompt Leakage
System prompts dictate how the LLM “behaves”, and if these prompts become “leaked” to attackers, they can experiment with different inputs and find loopholes, or disable certain features that should be considered “safe”. Protecting system prompts is very important since they usually grant “keys” into the application’s logic.
8. Vector & Embedding Weaknesses
Most LLM applications use vector databases to store and/or retrieve information in an efficient manner. If a vector database is not secured sufficiently, attackers can steal sensitive embeddings or insert malicious embeddings. In either circumstance, the privacy of the data disclosed can be jeopardised, which may compromise the accuracy of the model’s response.
9. Unfettered Trust in LLMs
While it’s true that LLMs are very powerful, they are not infallible. If a business trusts every response from an LLM, it can lead to poor or misguided decision-making, the distribution of misinformation, and/or failure to comply with acts and regulations. Humans are still necessary to “catch” errors/irregularities and provide continuity in the outputs.
10. Model Theft & Reverse Engineering
LLMs are valuable intellectual property, and they can be stolen. An attacker may attempt to steal a model by trying to copy or reverse engineer it through repeated queries or by insider leaks. Once stolen, it can be useful for competitors or malicious actors. Models can be re-purposed for malicious use; therefore, any financial or reputational damage can be catastrophic.
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5 Best Practices for LLM in Cyber Security in 2025
In 2025, securing large language models involves more than firewalls and passwords. The interaction model between LLMs and users, data, and systems involves immense ambiguity that adds unique levels of risk. The optimal path involves combining robust cybersecurity best practices with AI-specific mitigations. Following best practices, organisations can proactively reduce potential exposure, stay compliant, and satisfy trust issues. Below are five core LLM security best practices that every organisation employing LLMs should establish.
1. Strong Access Control & Authentication
Don’t allow your LLMs to be open to the world. You should only allow users and applications access to your LLMs through multi-factor authentication and role-based access. You should also utilise network segmentation to isolate the LLM environment from your critical systems and other projects to ensure attackers cannot simply commandeer bad actors or misuse your model.
2. Input Validation & Output Filtering
Every time you provide an input for an LLM, treat it as potentially harmful. Filters should be applied early on to remove harmful prompts or unexpected code from user prompts. Similarly, validate and filter outputs before use in downstream applications. This step is critical to ensure the usage of LLM outputs does not allow prompt injection, harmful payloads, or hazardous recommendations to bypass user validation.
3. Security Testing & Red-Teaming
Machine Learning models and LLMs should be continuously stress-tested to discover weaknesses. Red-teaming, LLM penetration testing or simulating real-world attacks (e.g., prompt injection, jailbreak attempts), probes for weaknesses like social engineering, command and control, and prompt injection. Organisations are encouraged to run red-team tests to fix vulnerabilities before real-world usage. Red-teaming is analogous to penetration testing for AI systems.
4. Data Governance & Privacy-by-Design
Given that LLMs typically deal with sensitive information, an organisation’s data governance must be ironclad. Encrypt training and inference data, anonymise user inputs, and avoid putting personal identifiers into models. Privacy-by-design helps ensure compliance with the regulations around data retention (like GDPR) and U.S. AI guidelines and protects customer trust from a data privacy breach.
5. Continuous Monitoring & Incident Response
Monitoring is necessary even when there’s no substitution for properly designed systems and defensible architecture. Record logs, warnings, errors, and other anomalies that could suggest abuse or exploitation of the overall architecture.
If the organisation supplements continuous monitoring with a comprehensive incident response turn-key policy, the organisation can execute its incident response with minimal wasted effort once something goes awry. Continuous monitoring will help prevent minor leaks and exploitable elements from becoming a catastrophic breach on a global scale.
See how businesses secured their LLMs with LLM API security testing and protection – Read our case studies.
Conclusion
The emphasis in LLM security in 2025 is to be thoughtful and proactive. The threats are real, from accessible servers and image-based malware to misaligned AI and jailbreaking. Being confident that you have access controls, validation and testing, data hygiene, and monitoring means you have the framework to secure your models and your reputation. As LLMs become more sophisticated, our security must be increasingly sophisticated. Be vigilant—and safe.
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FAQ’s
1. What is an LLM in security?
In the context of security, LLM (Large Language Model) refers to systems like ChatGPT that can process natural language. When we talk about LLM security, we are discussing ways to protect LLMs from misuse, data leaks, or manipulative uses. In effect, security LLM means facilitating the safe use of AI, without exposing sensitive data or creating vulnerabilities.
2. What is a secure LLM?
A secure LLM is a language model that has safeguards built into it and is deployed with safeguards. A secure LLM will have access control, data protection, monitoring, filtering to avoid harmful outputs, etc. The goal of having a secure LLM is to keep the model reliable, safe, and compliant with any regulatory requirements.
3. What are LLM attacks?
LLM attacks are attempts to manipulate or exploit language models for negative outcomes. Its attacks might include prompt injections, data poisoning, or gaining sensitive information by forcing an LLM to reveal helpful language model responses. LLM attacks are attempting to take advantage of LLMs’ proprietary method of working with and generating text.
4. What are the risks of LLM?
The risks include: data leakage, biased outputs, manipulation of the model, and reliance on AI response as opposed to having and using human brain processes. In addition, an attacker may steal or reverse engineer a model with the intent of malicious use. Further, without safeguards in place, LLMs can have potential incursions for security and compliance.
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