Generative AI Security
Protect your GenAI-powered applications and solutions with diverse generative AI security services
Embrace GenAI while mitigating security risks
Generative Artificial Intelligence (GenAI) is rapidly transforming industries as organizations integrate Large Language Models (LLMs) into their services and products. The transformative power of these technologies is clear: off-the-shelf models, customized pre-trained solutions, and proprietary systems all carry real benefits.
As GenAI accelerates innovation, it’s important to be aware of the cybersecurity risks beyond the hype.
Most GenAI risks stem from how AI models integrate into systems and workflows, not from the models themselves.
Overlooking these risks can expose your organization and customers to data breaches, unauthorized access, and compliance issues.
As a leading cybersecurity assurance testing company, we can help you tackle practical generative AI security risks when integrating GenAI into enterprise systems and workflows. We have extensive experience in helping organizations adopt new technologies such as GenAI and LLMs.
Common pitfalls in generative AI security
GenAI risks don’t exist in isolation. They are shaped by how the technology is applied in the organization. When building GenAI and LLM integrations, it’s crucial to consider security risks and build strong safeguards in place from the start.
Below are the most common security pitfalls we see when businesses adopt AI.
Jailbreak and prompt injection attacks
Malicious actors attempt to “jailbreak” an LLM by injecting carefully crafted prompts, tricking it into executing unauthorized actions or revealing sensitive information.
Excessive agency and
malicious intent
GenAI systems with excessive agency get manipulated by attackers (via jailbreak and prompt injection attacks) causing the system to execute malicious actions and posing significant security risks.
Insecure tool/
plugin design
When tools, plugins, or integrations for LLMs are poorly designed or insecurely implemented, they can introduce significant vulnerabilities leading to unauthorized access and data breaches.
Insufficient monitoring, logging, and rate limiting
Inadequate monitoring, logging, and rate-limiting mechanisms hinder the detection of malicious activity, making it challenging to identify and respond to security incidents promptly.
Lack of
output validation
Failure to validate and sanitize the output from GenAI models can lead to the disclosure of confidential information or the introduction of client-side vulnerabilities like Cross-Site Scripting (XSS).
Ensure the security of your LLMs and GenAI solutions
Our consultants can help you find and address cyber risks throughout the GenAI integration process. From planning to deployment, we’re there every step of the way.
We support secure adoption by assessing potential flaws in GenAI integrations and their interaction with your systems and workflows. We also provide recommendations for secure deployment.
Depending on your use case, the different assessment approaches may include any of the below. Contact us to discuss the best approach for your specific case.
Governance, risk and threat modeling for AI
Services to support you in the planning phase.
AI Governance
Defining the AI adoption objectives and acceptable use cases.
Adapting or creating ad-hoc risk management frameworks based on your organization’s needs and regulatory requirements.
AI Risk Modeling
Identifying and prioritizing generative AI security risks at an organizational and use case level.
Creating a shared risk understanding between development teams, cybersecurity, and business units.
AI Threat Modeling
Identifying the most relevant attack paths based on risk prioritization technical analysis.
Identifying control gaps and prioritizing control implementations through cost/ benefit analysis.
Implementation and integration of AI solutions
Services to support you in the implementation phase.
Pentesting LLM Applications
Identifying and addressing the cybersecurity weaknesses in your organization’s LLM applications and integrations.
Understanding the exploit vulnerabilities and specific risks of LLM applications, the specific cyber risks they pose, and the attacker goals that will most likely lead to being targeted.
Pentesting AI-supporting Infrastructure
Identifying high risk attack paths leading to your AI-powered applications and offering recommendations to protect these.
Ensuring secure hosting and AI-management, protecting AI data and access points.
LLM applications security canvas
Our LLM Application Security Canvas captures our battle-tested approach to deploying LLM applications to production securely. It implements security controls across every stage of the pipeline.
Download the LLM Application Security Canvas here.
Spikee: Open‑source LLM application security testing
As organizations embed LLM agents into workflows, prompt injections pose an increasing risk to systems. To address gaps left by existing approaches to LLM application security, we developed Spikee.
Spikee is our open‑source tool for LLM application security testing. It’s specifically designed for assessing real cybersecurity threats such as data exfiltration, cross‑site scripting (XSS), and resource exhaustion.
The tool includes modifiable attack scripts, dataset generation for systematic guardrail evaluation, and support for local inference and API‑based targets.
We can help
Reversec provides industry‑accredited cybersecurity assurance and consulting, backed by 30+ years of experience. We understand the challenges that emerge when designing and implementing AI and LLM solutions.
Our consultants can help you design, build, and run AI solutions on secure, resilient infrastructure.
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