Prompt Injection
Direct and indirect prompt injection, instruction hierarchy, context manipulation, jailbreak resistance, and attacks delivered through retrieved or connected content.
AI Application Security
Test how your LLM application behaves when its instructions, data, and tools are attacked.
TruSec evaluates LLM-powered applications, retrieval pipelines, integrations, and agentic workflows for prompt injection, sensitive-data exposure, excessive agency, unsafe output handling, and weaknesses in the surrounding web and API controls.
Service Overview
Assess the complete AI-enabled application—not just the model endpoint.
LLM applications combine probabilistic model behavior with system prompts, retrieved content, user data, tools, APIs, identities, and conventional application code. Security failures often emerge at those trust boundaries: untrusted content changes instructions, retrieved documents expose data, an agent invokes a tool with excessive authority, or model output reaches another system without adequate validation.
TruSec uses controlled adversarial testing to evaluate the deployed system in its business context. Coverage is tailored to the application architecture, model access, data sources, user roles, tool permissions, safety requirements, and approved test environment. The objective is to produce reproducible evidence and practical remediation—not to claim that every possible model response can be eliminated.
Assessment Coverage
Coverage across model behavior, application controls, data, and agency.
Testing is aligned to the current OWASP GenAI risk areas and adapted to the actual architecture and authorized capabilities.
Sensitive Information Disclosure
Exposure of system instructions, conversation data, retrieved content, secrets, tenant information, personal data, and other information the user should not receive.
RAG and Data Boundaries
Document authorization, retrieval isolation, vector and embedding weaknesses, source attribution, poisoned content, and cross-user or cross-tenant access.
Tools and Excessive Agency
Function calling, plugins, connectors, agent permissions, unintended actions, approval controls, identity propagation, and opportunities to misuse authorized tools.
Output and Integration Security
Unsafe rendering or execution of model output, downstream injection, insecure output handling, external requests, and trust placed in generated content.
Application and API Controls
Authentication, authorization, sessions, rate and cost controls, file handling, APIs, logging, abuse resistance, and conventional weaknesses surrounding the AI feature.
How the Engagement Works
A controlled assessment built around realistic misuse paths.
Map the AI system
Confirm models, prompts, roles, data sources, retrieval paths, tools, integrations, environments, and critical business actions.
Define abuse cases
Identify sensitive data, prohibited outcomes, authorization boundaries, high-impact actions, safety constraints, and expected controls.
Test and validate
Exercise approved adversarial scenarios manually and document repeatable evidence when a weakness produces meaningful impact.
Report and retest
Connect findings to the affected trust boundary, recommend layered controls, and validate corrected behavior when included.
Deliverables
Evidence that engineering, security, and leadership can use.
Reporting distinguishes model behavior from application vulnerabilities and identifies the control layer best positioned to reduce risk.
- Executive summary and AI application risk posture
- Architecture, roles, data sources, tools, and tested boundaries
- Reproducible adversarial scenarios and supporting evidence
- Affected users, data, integrations, or business actions
- Likelihood, impact, and environmental prerequisites
- Layered remediation and guardrail recommendations
- OWASP LLM and GenAI risk mapping where relevant
- Retest status for corrected findings
Material issues are escalated during testing according to the agreed communication plan. Reports avoid including sensitive prompts or data beyond what is necessary to reproduce and correct a finding.
Common Questions
LLM and generative AI testing questions
What types of AI systems can be tested?
Testing can cover customer or workforce chatbots, retrieval-augmented generation systems, copilots, model-backed application features, and agents that use tools or connectors. Scope depends on the deployed architecture and available test access.
Is this only prompt-injection testing?
No. Prompt injection is important, but the assessment also evaluates data access, retrieval boundaries, system-prompt exposure, tool permissions, output handling, identity and authorization, rate controls, APIs, and the surrounding application.
Can you guarantee that an LLM cannot be jailbroken?
No responsible assessment can guarantee every future model response. Testing identifies reproducible weaknesses, meaningful abuse paths, and control improvements that reduce likelihood and impact within the evaluated system and conditions.
What is useful for scoping?
Useful inputs include the application and user roles, model providers, system components, RAG data sources, tools or connectors, high-impact actions, test environment, prohibited outcomes, and approximate number of workflows. Sensitive prompts, credentials, and private data should be exchanged only through an approved secure channel.
Related Services
Build coverage around the full attack path.
Preparing an LLM application or AI agent for real users?
Share the application type, user roles, model and retrieval architecture, tools or connectors, sensitive data boundaries, and desired testing window—without including secrets or sensitive prompts.