AI/LLM Application Testing
Successful artificial intelligence is backed by real confidence.
Take your organisation to the next level by securely deploying LLM-powered features and applications through bespoke LLM penetration testing.
What is AI/LLM Application Testing
and why does it matter?
A Large Language Model (LLM) application test is a structured engagement that simulates real-world attackers interacting with an LLM-enabled system to manipulate its behaviour, gain unauthorised access to sensitive data or abuse its capabilities.
During a test, SECFORCE will assess the LLM-enabled application using both manual and automatic methods that cater for the non-deterministic nature of LLMs, resulting in increased visibility of your system's security posture and associated business risks.
With 57%* of organisations citing lack of trust as a major concern regarding AI adoption, SECFORCE's AI/LLM Application Testing helps you build confidence in your production deployments by replicating realistic attack scenarios.
Outcomes of AI/LLM Application Testing
LLM-connected data protection and compliance
Validate and demonstrate to auditors that your system does not handle or expose sensitive data - including credentials, PII or internal documents - even when faced with crafted inputs.
Jailbreak, misuse and prompt injection resistance
Demonstrate that attackers cannot coerce the LLM to execute restricted functions, bypass controls, or perform unrelated activities and assess the effectiveness of guardrails, system prompts, and policies against direct and indirect prompt injection.
eDOS resilience
Identify and remove economic Denial of Service (eDOS) attack vectors and stop inputs triggering unbounded consumption of resources (computational or financial).
Secure outputs
Ensure LLM output cannot be used to damage your organisation by verifying how the application consumes, sanitises, and trusts model responses.
Who can benefit from AI/LLM
Application Testing?
31%** of AI-related security incidents lead to operational disruption. Whatever LLM model provider you use (OpenAI, Anthropic, Meta, open-source or custom models), the system it is connected to will likely face attacks designed to exploit known LLM vulnerabilities.
Any organisation using an LLM model to power internal tools or customer-facing applications should proactively test for exploitable vulnerabilities before and during deployment.
Testing scenarios
Validating the security of a customer-facing chatbot or adviser before launch.
Vetting the security risks of an internal application to reduce the risk of insider attacks that could misuse API access.
Reviewing RAG connections to SharePoint, drives and wikis.
Verifying cost controls, quotas and rate limits for public-facing LLM-enabled applications before scaling usage-based billing.
Hardening document-ingestion workflows to block indirect prompt injection from uploaded files that could be used to expose data.
Sanitising front-end rendering paths to eliminate traditional web application vulnerabilities from LLM responses.
Regular AI/LLM Application Testing is also highly recommended with increased AI/LLM usage.
The SECFORCE way
SECFORCE approaches AI/LLM-enabled applications with a true attacker's mindset shaped by decades of offensive security experience.
Our AI/LLM security assessments simulate attack scenarios that determine the technical and business impact of a real vulnerability being exploited. We combine manual testing with targeted automation to expand coverage. This approach also helps reveal issues that might not appear every time, ensuring more consistent and reliable results.
We provide you with detailed reporting and tailored remediation advice to reduce the likelihood of real-world exploitation, support regulatory compliance, and help ensure that LLM-powered systems are secure, resilient and aligned with business objectives.
