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Security testing for LLMs

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Minimize the risks of your LLMs

Our LLM pentesting service is designed to evaluate the resilience and security of your models and systems, protecting their integrity and confidence in your results.

OWASP Top 10 for
LLM Applications

We use OWASP which provides guidance on the most common and dangerous vulnerabilities in LLMs and allows developers to take preventative measures to secure their learning models.

This manipulates a large language model (LLM) through crafty inputs, causing unintended actions by the LLM. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources.

This occurs when LLM training data is tampered,
introducing vulnerabilities or biases that compromise
security, effectiveness, or ethical behavior. Sources
include Common Crawl, WebText, OpenWebText, & books

LLM application lifecycle can be compromised by
vulnerable components or services, leading to security attacks. Using third-party datasets, pre- trained models, and plugins can add vulnerabilities.

LLM plugins can have insecure inputs and insufficient
access control. This lack of application control makes
them easier to exploit and can result in consequences like remote code execution.

Systems or people overly depending on LLMs without
oversight may face misinformation, miscommunication, legal issues, and security vulnerabilities due to incorrect or inappropriate content generated by LLMs.

This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution

Attackers cause resource-heavy operations on LLMs,
leading to service degradation or high costs. The
vulnerability is magnified due to the resource-intensive nature of LLMs and unpredictability of user inputs.

LLMs may inadvertently reveal confidential data in their responses, leading to unauthorized data access, privacy violations, and security breaches. Its crucial to implement data sanitization and strict user policies to mitigate this

LLM-based systems may undertake actions leading to unintended consequences. The issue arises from
excessive functionality, permissions, or autonomy granted to the LLM-based systems

This involves unauthorized access, copying, or exfiltration of proprietary LLM models. The impact includes economic losses, compromised competitive advantage, and potential access to sensitive information

Integrity of Results

Ensures that AI models provide reliable answers and are free from external manipulation.

Privacy Protection

Safeguards sensitive information handled in AI models, complying with privacy and security regulations.

Emerging Attack Preparedness

We identify potential points of failure in your systems to protect them against sophisticated and novel attack methods.

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