How App Integrations Expand the Attack Surface of LLMs – and How to Secure Them

Large language models or LLMs are the core engines that drive generative AI technology, allowing computers to understand human language and respond with natural-sounding, human-like text. With applications ranging from content generation and chatbots to translations, text summarization, and sentiment analysis, LLMs effectively drive natural and seamless interactions between humans and machines.

LLM’s capabilities extend even further with app integrations. Integrating pre-trained or fine-tuned LLMs into enterprise applications opens up a wide range of use cases for organizations while simultaneously expanding the attack surface.

So what are the risks of LLM-app integrations? And more importantly, how can you mitigate these risks?

The Risks of Integrating LLMs into Enterprise Apps

Integrating LLMs with enterprise applications enables organizations to directly embed LLMs into operations for a wide range of use cases. These integrations can create greater operational efficiencies and enhance employee productivity, unlock better data insights, improve decision-making, and gain a competitive edge.

That said, these integrations also create certain security risks. The key risks are:

Data Loss

LLMs can be connected to a range of enterprise apps, including CRMs, ERPs, and ticketing platforms. Through these integrations, an LLM may be able to access sensitive or confidential data, such as customer or employee records, financial information, or proprietary documents. If the integration grants the LLM extensive permissions, the LLM may be able to make irreversible changes to data, including modifications, deletions, or overwrites, which could result in the corruption of important datasets or even permanent data loss.

Prompt Injection Attacks

In a prompt injection attack, attackers give malicious instructions disguised as legitimate prompts that “trick” the LLM into behaving in an unintended or harmful way, for example, to reveal sensitive information to the attacker. These attacks can also be used in an attempt to get an LLM to extract internal system information, direct legitimate users to a phishing website, generate offensive responses, or spread misinformation.

Unauthorized Actions

When an LLM is integrated with enterprise apps in a way that allows it to perform actions, those actions may have unintended consequences on business processes or workflows. A malicious prompt is one way to get an LLM to perform unauthorized actions. But even legitimate prompts can result in unintended or harmful actions.

If a legitimate prompt is ambiguous or incomplete, the LLM may issue the wrong command to the connected app, leading to data deletions or corruption, permission changes, or the closure of helpdesk tickets. These errors can adversely affect user productivity, disrupt business operations, and in some cases, lead to financial losses and reputational damage.

Supply Chain Vulnerabilities

LLM-app integrations often rely on third-party APIs, SDKs, plugins, and connectors to ensure seamless connectivity and data-sharing. However, these third-party elements in the software supply chain may also introduce new vulnerabilities into an organization’s IT ecosystem.

For example, open-source libraries may have unpatched zero-day vulnerabilities that allow adversaries to compromise the LLM’s data flow. They may also be able to intercept, manipulate, or steal the data exchanged between LLMs and apps, or gain extended privileges to harm enterprise assets or data.

Strategies to Minimize the Risks of LLM-App Integrations

GenAI technology and LLMs are both still evolving, so the risks of integrating LLMs with apps are still very much prevalent. Fortunately, it is possible to mitigate these risks and reduce the size of the LLM attack surface with these strategies:

Access Controls and Least-Privilege Permissions Can Minimize the Risk of Data Losses

Data access controls limit what LLMs can see, change, or delete when they are integrated with enterprise apps. Least-privilege permissions ensure that the LLMs only have the minimum access to data required to complete their tasks – no more and no less. These boundaries can reduce the probability of data leaks and losses.

Input Sanitization Can Prevent Prompt Injection Attacks

To prevent prompt injection attacks, it’s crucial to sanitize all user inputs before they reach the LLM. Methods like pattern matching and named entity recognition (NER) should be used to validate user inputs and prevent the LLM from processing malicious instructions. This can reduce the risk of unauthorized or malicious actions in integrated apps. Input sanitization also removes sensitive data before it reaches the LLM and lowers the probability of hallucinated responses.

EASM Can Prevent LLMs from Performing Unauthorized Actions

External Attack Surface Management (EASM) is an effective strategy to prevent LLMs from taking unauthorized actions. EASM tools look for unusual inputs that may indicate LLM manipulation attempts and provide actionable insights that help security teams react swiftly to potential threats. They can also adjust input filters and fine-tune model responses to control the LLM attack surface, monitor API traffic for signs of abuse, and leverage AI-driven behavior analysis to prevent LLM exploitation.

Penetration Testing Can Help Identify Supply Chain Vulnerabilities

LLM pentesting is an effective way to identify vulnerabilities in LLMs integrated with business applications. Skilled pentesters test many different aspects of LLMs, including the risks highlighted in the OWASP Top 10 list for LLMs. At the baseline, they will look for:

• Outdated or deprecated components
• Hidden biases
• Backdoors
• Weak model provenance
• Vulnerable LoRA adapters

Pentesters may also use adversarial inputs to assess the model’s response, look for API misconfigurations, and test LLM integrations for authentication flaws, improper data handling, rate-limiting weaknesses, and other vulnerabilities – all of which can help to mitigate supply chain risks.

Adversarial Exposure Validation (AEV) Can Uncover Real-World LLM Exploitation Paths

AEV goes a step beyond penetration testing by validating how an attacker would exploit an LLM integration across the full kill chain from prompt injection and privilege escalation to API abuse and data exfiltration. AEV uses generative AI to “think” like an attacker and autonomously probes LLM-driven workflows, connected applications, identity paths, and third-party components to reveal how seemingly minor vulnerabilities and misconfigurations can be chained into real exploitation scenarios.

AEV gives security teams the ability to continuously mitigate critical risks before attackers can discover and exploit them themselves.

Secure Your LLM-App Integrations with Continuous, Evidence-Backed Validation

LLM-app integrations can enable powerful automations, accelerate decision-making, and streamline operations for organizations, but security teams cannot ignore the new exposure paths they introduce. Securing these integrations requires continuous validation, real-world adversarial testing, and a clear understanding of how LLM-driven workflows behave, especially under pressure in unique scenarios.

BreachLock delivers Penetration Testing as a Service (PTaaS), Adversarial Exposure Validation (AEV), and continuous pentesting and red teaming, ideal for enterprise Continuous Threat Exposure Management (CTEM) programs in the AI era. Our evidence-backed offensive security solutions help you map your LLM attack surface, uncover high-impact vulnerabilities across apps, APIs, and beyond, and validate your defenses against real-world attack paths. With BreachLock, you can adopt LLM-app integrations with more confidence and safely turn AI innovation into a competitive advantage.

To learn more, contact BreachLock today.

About BreachLock

BreachLock is a global leader in offensive security, delivering scalable and continuous security testing. Trusted by global enterprises, BreachLock provides human-led and AI-powered Attack Surface Management, Penetration Testing as a Service (PTaaS), Red Teaming, and Adversarial Exposure Validation (AEV) solutions that help security teams stay ahead of adversaries.

With a mission to make proactive security the new standard, BreachLock is shaping the future of cybersecurity through automation, data-driven intelligence, and expert-driven execution.

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BreachLock Labs