LLMs Under Siege: Securing the Expanding AI Attack Surface with EASM

Introduction

Large Language Models (LLMs) have become integral to enterprise applications, revolutionizing industries by automating customer interactions, generating content, and supporting data-driven decision-making. As organizations increasingly integrate LLMs into their workflows, they gain powerful capabilities for automation, data processing, and user interaction.

However, this adoption also introduces a vastly expanded attack surface. Unlike traditional applications, LLMs process and respond to natural language inputs, making them susceptible to a unique set of security risks. Threat actors can manipulate LLMs through adversarial prompts, data poisoning, API exploitation, and more, making External Attack Surface Management (EASM) essential for securing these AI-driven assets.

For security leaders, mitigating these risks requires a proactive approach—one that leverages EASM to continuously identify, assess, and prioritize vulnerabilities. This blog explores the evolving attack surface of LLMs, the top 5 challenges that LLMs pose to security teams, and how EASM addresses these evolving threats. We also discuss what these risks mean for CISOs, regulatory and compliance considerations in enterprise environments, and provide best practices for securing LLMs.

Top 5 Security Challenges with LLMs

1. Expanded Attack Surface and Adversarial Prompts

Unlike traditional applications, LLMs can be manipulated through natural language inputs. Threat actors exploit this flexibility to trigger unintended model behaviors, bypass content moderation, or extract sensitive data. This creates a vast attack surface where vulnerabilities may not be immediately apparent.

How EASM Solves This:

• EASM tools monitor prompt patterns for unusual inputs that indicate manipulation attempts.
• Security teams can use EASM insights to adjust input filters and fine-tune model responses.
• EASM combined with penetration testing can evaluate how LLMs respond to adversarial prompts, flagging potential vulnerabilities before they are exploited.

2. Data Poisoning and Information Leakage

The datasets used to train LLMs often include vast amounts of publicly available information, some of which may contain sensitive or proprietary data. If the data is not adequately sanitized, an attacker could use carefully designed prompts to extract confidential details. Additionally, adversarial actors can inject poisoned data into training sets, influencing the model to produce misleading or malicious outputs.

How EASM Solves This:

• EASM tools conduct deep scans to identify publicly accessible LLM endpoints or API-based LLMs and monitor for exposed sensitive data.
• Data privacy assessments can be conducted to analyze training data sources and highlight potential risks before models go live.
• Continuous penetration testing and monitoring of LLM outputs ensures that sensitive data is not being inadvertently leaked.

3. Prompt Injection Attacks

Attackers can craft specific prompts to manipulate an LLM’s behavior beyond its intended function. This can include:

• Generating unauthorized responses.
• Extracting internal system information.
• Bypassing content moderation filters.
• Coercing the model into executing harmful actions.

These attacks exploit the flexible nature of LLMs, making it essential for organizations to monitor input validation and response patterns closely.

How EASM Solves This:

• EASM continuously monitors interactions with LLMs to identify prompt injection attempts.
• Real-time alerting enables security teams to react swiftly to potential threats.
• AI-driven behavior analysis helps detect anomalies in model responses, preventing successful exploitation.

4. API-based LLM Vulnerabilities

LLMs are frequently accessed via APIs integrated into various applications, from customer support chatbots to business intelligence tools. If these APIs are not properly secured, attackers can exploit them to gain unauthorized access to the model, manipulate its outputs, or flood it with malicious queries that degrade performance.

How EASM Solves This:

• EASM tools identify exposed APIs and assess their security configurations to ensure proper authentication is in place.
• Security teams can monitor API traffic for signs of abuse, such as excessive queries or unusual request patterns.
• Continuous scanning detects outdated or misconfigured API endpoints that could be exploited by attackers.

5. Complexity of Natural Language Attacks

Unlike traditional input validation techniques that detect code-based threats, malicious prompts in natural language are more subtle and harder to filter. Adversarial language attacks continue to evolve, requiring advanced monitoring and mitigation strategies.

How EASM Solves This:

• AI-driven EASM solutions analyze trends in adversarial attacks, helping security teams stay ahead of evolving threats.
• Linguistic anomaly detection helps flag inputs that may indicate an attack attempt.
• Automated adversarial penetration testing allows security teams to simulate attacks and refine security controls accordingly.

Regulatory and Compliance Considerations

As LLMs become a staple in business operations, organizations must ensure compliance with various AI-related regulations and data privacy laws. Key considerations include:

• GDPR and CCPA: LLMs processing personal data must comply with data protection regulations to prevent unauthorized exposure.
• EU AI Act & NIST AI Risk Management Framework: Organizations integrating LLMs must assess risks related to bias, fairness, and explainability while ensuring secure deployments.
• Industry-Specific Standards: Financial institutions must comply with PCI DSS, while healthcare applications must adhere to HIPAA regulations when utilizing LLMs.

EASM helps organizations navigate these regulatory requirements by continuously monitoring AI-related assets, ensuring proper access controls, and identifying compliance gaps.

What This Means for CISOs

For CISOs and security leaders, the rise of LLMs represents both an opportunity and a challenge. The flexibility and intelligence of LLMs enhance business operations, but they also create new, unpredictable risks. Security teams must evolve their strategies to secure these AI-driven systems effectively.

Best Practices for Securing LLMs with EASM

1. Implement Continuous Monitoring: Regularly assess LLM interactions to detect malicious prompts, unauthorized access attempts, and anomalous behaviors.
2. Strengthen Authentication and Access Controls: Use strong authentication mechanisms such as multi-factor authentication (MFA), role-based access control (RBAC), and API security measures to limit who can interact with LLMs.
3. Develop Robust Input Filtering: Deploy advanced filtering mechanisms to detect and mitigate adversarial inputs, including prompt injections.
4. Regularly Audit Training Data: Ensure that training datasets do not contain sensitive or proprietary information that could be exposed through model outputs.
5. Integrate EASM and Penetration Testing into Security Workflows: Leverage EASM and penetration testing to gain real-time visibility into exposed LLM assets, monitor API vulnerabilities, and identify new attack vectors.
6. Prepare Incident Response Plans: Establish protocols for responding to LLM-based security incidents, including containment, investigation, and mitigation strategies. This can be established by using continuous automated red teaming.

The Role of EASM in Securing LLMs

To mitigate the risks associated with LLM adoption, organizations must integrate EASM into their security strategy. EASM provides a continuous, external perspective of an organization’s attack surface, enabling security teams to identify, monitor, and mitigate threats proactively. Key benefits include:

• Identifying Exposed LLMs: EASM scans the internet to locate publicly accessible LLM endpoints, ensuring that organizations are aware of any unintended exposure.
• Monitoring Input Validation: By analyzing incoming prompts, EASM helps detect malicious attempts to manipulate the model.
• Conducting Data Privacy Assessments: EASM enables organizations to analyze the data sources used to train LLMs, helping identify potential privacy risks.
• Strengthening Access Control and Authentication: EASM helps enforce strong authentication mechanisms, such as API tokens, MFA, and RBAC, to restrict access to LLMs.
• Monitoring Model Behavior in Real Time: Continuous monitoring with EASM allows security teams to detect unusual or potentially malicious responses.

Conclusion

As LLMs become a central component of enterprise applications, their expansive attack surface presents a growing challenge for security teams. The dynamic nature of LLM vulnerabilities—ranging from prompt manipulation to API exploits—demands a proactive, offensive security strategy. External Attack Surface Management (EASM) plays a pivotal role in securing LLMs by providing real-time visibility, identifying exposed endpoints, monitoring for malicious prompts, and ensuring robust access controls.

For CISOs and security leaders, the message is clear: by integrating EASM to secure LLMs, organizations can strengthen their defenses against the evolving threats that target LLM-based applications. Investing in EASM is not just about protecting LLMs—it’s about safeguarding the entire digital ecosystem from emerging AI-driven risks.

Author

Ann Chesbrough

Vice President of Product Marketing, BreachLock