LLM Data Leakage: Definition, Risks & Best Practices To Prevent It

A Large Language Model (LLM) typically encounters a leakage when sensitive, confidential, or regulated information is exposed without appropriate authorization. This usually happens when an employee accidentally pastes sensitive data into prompts, when an LLM retrieves restricted information from connected systems, when sensitive data appears in outputs, or when chat logs, training data, or AI infrastructure are not properly secured.

In any case, the threat of LLM data leakage is by no means only theoretical. IBM’s 2025 Cost of a Data Breach report found that almost 13% of organizations had reported breaches that involved AI models, with 97% citing inadequate AI access controls. The critical nature of this threat is easy to see when figuring in a typical workplace scenario where an engineer uses an LLM to debug source code, a sales employee pastes customer notes into a chatbot to draft a follow-up, or a legal team uploads a contract to summarize obligations.

In each of these scenarios, the usage may seem innocuous and even completely in line with their daily responsibilities. Still, if the data being entered is processed, stored, retrieved, or exposed outside approved controls, the organization may face security, privacy, contractual, and regulatory consequences.

Examples of LLMs Leaking Data

Some recent cases that further highlight why organizations should take LLM data leakage so seriously include the following:

A. Samsung

When talking about potential LLM data leakages, it’s hard not to think of Samsung’s case at first, as it remains the most cited example in this instance. Back in 2023, during the “infancy” of LLM enterprise usage, employees at Samsung’s semiconductor business unit entered their source code related to defective equipment, as well as the recording of their internal company meeting, into ChatGPT.

When the leak was noticed, it caused a massive PR and regulatory nightmare for Samsung, but also highlighted how it didn’t require a sophisticated cyberattack for such information to be breached, but only an ordinary employee’s unsupervised use of AI tools. Moreover, it illustrated how confidential information worth billions can leak from the enterprise environment if an organization lacks clear AI usage policies, prompt-level controls, and monitoring.

B. ChatGPT

It wouldn’t be far-fetched to claim ChatGPT is the poster boy of LLMs. And yet, in March 2023, OpenAI briefly had to take ChatGPT offline after a bug in its open-source library allowed users to see titles from another active user’s chat history. This bug may have also exposed payment-related information to almost 1.2% of all ChatGPT Plus subscribers that were active during a nine-hour window, with information such as names, email addresses, payment addresses, credit card type, last four digits, and expiration dates all being exposed.

This incident highlighted how LLM data leakage can also be the result of faulty application infrastructure and not just model behavior, meaning even when the model isn’t intentionally revealing sensitive information, bugs in its support system, caching layers, payment workflows, logs, or user interfaces can expose such information anyway.

C. DeepSeek

One of ChatGPT’s biggest competitors had a LLM data leakage incident of its own when, in January 2025, it was reported that a publicly accessible DeepSeek ClickHouse database exposed over a million lines of log streams. The exposed data in these log streams included chat history, secret keys, backend details, API secrets, and operational information. At the same time, the database also allowed for full control over database operations without proper authentication.

This case highlighted how LLM data leakage can also happen through misconfigured infrastructure around the AI system, where chat histories, logs, credentials, or backend data stored in exposed databases can leak even if the chatbot appears to be secure to the end user.

How Data Leakage Happens In Large Language Models?

But how does LLM data leak even happen? Here are the top reasons:

A. Prompt Injection Attacks

Prompt injection attacks usually occur when a user or attacker gives LLM instructions that are designed to override their original rules or safety controls. An example would be an attacker asking the model to ignore all previous instructions, reveal some hidden system prompts, retrieve restricted files, or summarize confidential information that it should be exposing in the first place.

Such attacks can be most devastating when the LLMs are connected to enterprise tools, databases, emails, file repositories, or customer support systems. A model with access to such data and lacking in the ability to enforce permissions is highly vulnerable to malicious prompts tricking it into revealing information that no end user should be able to see.

Some mitigation measures to thwart such attacks include strong access controls, prompt filtering, output monitoring, and clear separation between what the model can access and what each user is authorized to view.

B. Training Data Contamination

Training data contamination occurs when sensitive, private, inaccurate, copyrighted, or unauthorized information becomes part of the data being used to train or fine-tune an LLM. Such data can include, but is not limited to, information such as customer records, employee data, internal documents, credentials, source code, or other confidential business information.

Once this data is included in the training data, there is the risk that the model will begin reproducing or exposing part of it in future outputs. Because it can generate responses that resemble or reveal data patterns, texts, or details from its training material, malicious actors may exploit this vulnerability.

Enterprises can mitigate such risks by carefully reviewing and sanitizing training datasets before using them, and removing personal data, secrets, regulated information, duplicates, and internal content that should not be part of the training dataset.

C. Adversarial Queries

Adversarial queries are crafted prompts that are designed to test, manipulate, and pressure an LLM into revealing information that it should be withholding. These queries are purposefully designed to be harmless at first glance, but are structured in a way that ensures they can bypass safeguards, extract hidden details, and make models disclose sensitive data.

An example would be a malicious actor asking the model to complete a partial API key, infer confidential information from context, role-play as an administrator, or provide “sample” customer data that resembles real records. These prompts can also be done sequentially and repeatedly to gradually extract information that may otherwise not be extractable in one single prompt.

These kinds of LLM data leakage are the hardest to manage because adversarial queries are creative. They mimic what organizations do internally as part of QA testing. As a result, the best practice to thwart them includes continuous monitoring, red-teaming, sensitive data detection, rate limits, and policy-based response controls.

Types Of LLM Data Leaks

LLM data leakage can occur in various ways. Some of the most common types include the following:

A. Prompt-Based Data Leaks

Prompt-based leaks happen when employees enter sensitive information directly into the LLM promptly. This includes documents such as customer records, internal strategy documents, legal summaries, source code, credentials, or financial information.

The model does not expose the data immediately, but the information is still getting processed, stored in logs, reviewed for monitoring, and retained in the background, all depending on the exact platform setting and contractual terms.

B. Output-Based Data Leaks

Output-based data leaks occur when an LLM generates a response that includes sensitive, confidential, or unauthorized information. This usually happens if the model retrieves information that is restricted, or data directly from the training dataset, or combines it with the available context, which leads to sensitive information being revealed.

This poses a significant risk for organizations as the output may appear legitimate and be copied into emails, reports, tickets, presentations, or customer communications. In the absence of proper reviews at this stage, the compromised leaked information will spread quickly beyond the original AI interaction.

C. Training Data Leaks

Training data leaks happen when sensitive or restricted information used during model training or fine-tuning appears in model responses. This can involve personal data, internal documents, proprietary code, financial data, or confidential business information.

This particular risk is highest during the fine-tuning phase, as organizations may end up using poorly optimized or reviewed datasets, where sensitive data is not removed or masked before it is fed into the model.

D. Retrieval-Based Data Leaks

Retrieval-based leaks occur when an LLM connected to enterprise data sources ends up retrieving information that it isn’t supposed to access. This is most common in RAG instances where the systems from which the model is pulling its information are compromised through its internal policies.

Hence, the issue isn’t always the model itself, but rather the access controls built around the tools, specifically around the data that the model is supposed to retrieve. Not defined properly, this leaks to LLM, exposing confidential information across teams, departments, and business units.

E. Connector & Plugin Data Leaks

Connector and plugin data leaks occur when an LLM is integrated with external applications, APIs, SaaS, and internal systems without proper security controls in place. These integrations then allow the model to read, summarize, transfer, and act on data from the connected environment.

If these tools are misconfigured, overpermissioned, and vulnerable to prompt injection, they can become susceptible to sensitive data exposure. Enterprises should carefully review each connector, as well as its access permissions, what actions it can perform, and whether its current permissions match expectations.

F. Credential & Secret Leaks

Credential and secret data leaks involve the exposure of information such as health data, biometric data, financial data, government identifiers, children’s data, and other regulated personal information. These leaks then created heightened privacy, compliance, and reputational risks for enterprises.

Organizations that use LLMs in HR, healthcare, financial services, customer support, or legal workflows must be especially careful, as the more sensitive the data, the stronger the controls need to be around its collection, processing, access, retention, and disclosure.

G. IP & Source Code Leaks

Intellectual property code leaks involve the exposure of proprietary algorithms, design documents, trade secrets, technical architecture, or source code. As expected, these risks are particularly relevant for engineering, product, legal, and research teams that rely on LLMs in their work.

Consider an employee uses unmanaged AI tools to review or generate code, summarize documents, or refine strategic material, confidential business information leaves the organizational environment. Hence, organizations must have clear rules on what IP can be used with LLMs and ensure technical controls are in place to enforce those policies.

How To Identify LLM Data Leakage

Identifying LLM data leakages requires extensive monitoring by organizations on how the data enters their infrastructure, moves, and then exits the AI systems. This process involves reviewing all user prompts, model outputs, connected data sources, chat histories, training datasets, and system logs for any unauthorized or sensitive information. They’re discussed in greater detail below:

A. Reviewing User Prompts

This should be the first step when it comes to identifying an LLM data leakage. Knowing and reviewing what information users are entering as LLM prompts is critical, as it may identify any accidental leakages, such as an employee entering customer records, internal documents, legal advice, source code, credentials, or personal data while trying to leverage AI capabilities.

An efficient way to approach this would be to look for patterns that suggest risky usage, such as repeated use of customer identifiers, financial details, health information, API keys, or confidential project names. Done properly, this can be extremely helpful in helping both security and governance teams understand whether any sensitive data was shared with LLMs in ways that warrant restriction.

B. Monitor Model Outputs

Organizations must rigorously review all responses generated by an LLM, as a data leakage may also occur if the model is producing personal data, internal business information, source code, credentials, or content that closely mirrors that from restricted documents.

Output monitoring in this case will help, as users may not always realize that the response contains sensitive information. Automated scans prove highly effective in such instances as they help detect regulated data, confidential terms, secrets, and other risky content before the output is copied, shared, or stored elsewhere.

C. Check Access To Connected Data Sources

Numerous enterprise LLMs are connected to internal systems, including cloud storage, email, ticketing tools, knowledge bases, databases, and collaboration platforms. While these connections allow for unprecedented levels of collaboration and productivity, if not properly controlled, the model may retrieve data that the user was never authorized to access at all.

To both identify and remedy this risk, organizations need regular reviews of which systems the LLMs can access, with what permissions, and whether these permissions are appropriate for each user’s role. As a rule of thumb, any LLM should only be able to retrieve information that the requesting user is already allowed to view.

D. Audit RAG Pipelines

Retrieval-augmented generation (RAG) allows an LLM to answer questions using enterprise documents and data sources. However, a leakage may occur when the RAG pipeline ends up retrieving the wrong document, ignoring access controls, or includes sensitive information that was supposed to be redacted from LLM access.

To test whether the RAG system is leveraging such restricted files, outdated records, customer data, or confidential documents, or ignoring access permissions, organizations must review their internal policies on how documents are indexed, tagged, classified, and filtered before being made available to the LLM.

E. Scan Chat Histories & Logs

LLM conversations and system logs contain prompts, outputs, user details, timestamps, file references, and retrieved content. However, all such data is liable to become a potential source of data leakage if they contain sensitive information and is stored without proper controls in place.

Hence, organizations must scan chat histories and logs for any traces of personal data, credentials, regulated records, confidential business terms, and other restricted information. Moreover, they must also review who has accessed these logs, for how long, and whether all this data was protected via encryption and proper access controls.

F. Red Teaming Exercises

Through red teaming exercises and tests, an organization deliberately tests its LLM and puts it through a series of tests to assess whether it can be manipulated to expose sensitive data. These tests include prompt injection attempts, adversarial queries, role-play scenarios, or repeated questions designed to bypass safeguards.

At the end of such tests, organizations will have found weaknesses in their infrastructure before any attackers or internally careless users have had the chance to exploit them. Moreover, such red teaming exercises shouldn’t only cover the model itself, but all connected tools, plugins, RAG pipelines, access controls, and output filters.

Best Practices To Prevent LLM Data Leakage

Once an organization has understood all the issues that can lead to LLM data leakages, the most obvious next steps are to understand all the steps it can take to prevent such leakages. To that end, they can rely on the following best practices.

A. Establish Clear AI Usage Policies

Organizations need clear and concrete policies defining exactly what employees can and cannot do with their LLM access. This includes rules related to entering customer data, employee records, financial information, legal documents, source code, credentials, trade secrets, and other confidential business data into AI tools.

Implemented effectively, these policies help clarify which AI tools are approved, which use cases are allowed, who’s responsible for reviewing any new LLM deployments, etc. Most importantly, this helps curb any unmanaged tools and shadow AI, which lead to most LLM data leakages in the first place.

B. Discover & Classify Sensitive Data

Enterprises cannot protect their sensitive data assets if they don’t have a clear idea of where they exist. Hence, before any LLM is connected to enterprise systems, organizations must discover and classify all data across their infrastructure, including cloud storage, databases, SaaS applications, collaboration platforms, code repositories, and knowledge bases.

Doing so not only helps teams identify personal data, regulated records, credentials, intellectual property, and confidential business information before it is exposed to an LLM, but once such data is classified, organizations can deploy stronger controls and limit unnecessary AI access.

C. Enforce Data Minimization

LLMs must try to receive only the minimum amount of data necessary to complete an assigned task. Consequently, employees and applications should avoid sending full documents, complete customer records, large datasets, or unnecessary personal information when a simpler and more anonymized version of the same dataset would suffice.

This not only reduces the impact of potential LLM data leakage, but if sensitive data does enter the LLM environment, only a very limited part of it is exposed through prompts, outputs, logs, or connected workflows.

D. Implement Strong Access Controls

Access controls determine which data an LLM can retrieve and which users are allowed to see and access through the LLM. In case an employee does not have the permission to access a certain document, database, or customer records outside of the LLM environment, they should not have those permissions within the environment as well.

This is of particular importance in the context of enterprise copilots, chatbots, and RAG systems connected to internal data sources. Through role-based access controls, user-level permissions, least privilege access, and regular access reviews, organizations can meticulously prevent any unauthorized data exposure.

E. Secure RAG Pipelines & Enterprise Connectors

RAG systems create leakage risks if they retrieve sensitive or restricted data without proper filtering. Hence, organizations must ensure that RAG pipelines respect the existing permissions, exclude restricted content where necessary, and only return information that is both necessary and appropriate for the user and their use case.

The same principle extends to enterprise connectors and plugins, where any tool that allows an LLM to access email, cloud drives, databases, tickets, CRM records, or code repositories must be reviewed for all relevant permissions, data scope, authentication, logging, and security controls.

LLM Data Leakage & Regulatory Compliance

As you’d expect, LLM data leakage leads to serious regulatory compliance challenges for businesses. Personal, sensitive, and regulated data is potentially compromised through prompts, outputs, training datasets, logs, and connected enterprise systems. For organizations themselves, the issue isn’t only that the data is leaked, but the way it leaks. LLM data leakage signals a failure on the organization’s part when it comes to controlling how data is collected, accessed, processed, retained, or disclosed. It is easy to see a significant disparity with core data protection expectations that both regulators and customers have from organizations.

This risk is further elevated when LLMs process personal data through their model development, fine-tuning, or deployment phase. Regulators today, more than ever before, are paying closer attention to how AI systems use personal data, including whether organizations have a legal basis for processing, whether they respect users’ individual rights and privacy, and whether the personal data is being protected through appropriate measures and mechanisms throughout the AI lifecycle.

In the event of an LLM data leakage, organizations can expect a lot of corresponding events, as they can trigger breach notification requirements, incident response plans, and audit obligations. If an LLM exposes customer records, employee information, health data, financial data, credentials, or other regulated information, the organization needs to assess whether the incident qualifies as a data breach incident and the resulting responsibilities they are subject to.

In heavily regulated industries, this can only be the start of troubles, as compliance impact is much more severe. Healthcare organizations must protect electronically protected health information through a combination of administrative, physical, and technical safeguards that ensure the confidentiality, integrity, and availability of the data in question. If exposed by an LLM without the right safeguards, organizations are vulnerable to both privacy and security compliance issues.

This is also true for AI governance requirements, as AI regulations, just as data privacy requirements, have certain expectations and obligations placed on certain AI systems. This includes requirements around risk management, data governance, technical documentation, logging, transparency, human oversight, accuracy, robustness, and cybersecurity.

At the end, the key issue for organizations must be control. They need to know exactly what data their LLMs can access, whether users are allowed to use that data, how prompts and output data are monitored, whether sensitive data is blocked or minimized, and how AI activity is documented by the organization.

In the absence of such controls, LLM adoption increases regulatory risk across privacy, security, consumer protection, contractual confidentiality, and sector-specific compliance obligations.

How Securiti Helps

LLMs represent a remarkable upheaval for organizations in terms of productivity, automation, and more efficiency than ever before. However, these tools aren’t without their own sets of risks. Risks that pose financial, reputational, and operational consequences if not dealt with appropriately. As explained above, LLM data leakage is one of those risks.

But how do organizations go about solving it?

Securiti has the perfect solution for organizations that find themselves in this predicament.

Its Data Security Posture Management (DSPM) solution provides organizations with intelligent discovery, classification, and risk assessment of all their data. Such intelligent discovery and classification capabilities are vital in ensuring LLMs only receive data that they are supposed to receive and nothing that is strictly sensitive or IP.

Additionally, it enables real-time documentation as well as assessments to ensure organizations can continuously record proofs of their compliance without compromising on productivity or continuity of operations.

Request a demo today and learn more about how Securiti can help you ensure your organizational LLM usage complies with both regulatory and operational standards.

Frequently Asked Questions About LLM Data Leakage

Here are some of the most commonly asked questions related to LLM data leakage: