LLM and Chatbot Security Testing in Pakistan
LLM and chatbot security testing for prompt injection, retrieval leakage, unsafe tool access, and agent workflow abuse.
Every engagement is scoped before testing begins, with confidentiality expectations, safety boundaries, and communication paths agreed in advance.
Test an LLM SystemOverview
We test LLM-powered assistants, chatbots, and AI agents for realistic abuse paths across prompts, retrieval systems, tools, memory, permissions, and logging. Findings are framed for product and security teams so safeguards can be improved iteratively.
What we test / what we do
- Prompt injection and jailbreak resistance testing
- Retrieval and knowledge-base leakage checks
- Tool access and agent permission review
- Unsafe output and policy bypass testing
Risks reduced
- Prompt injection leading to unintended actions
- Leakage from retrieval-augmented systems
- Unsafe tool execution by AI agents
Process
- Map chatbot scope, model behavior, tools, data sources, and user roles
- Design abuse prompts and workflow scenarios
- Test retrieval, guardrails, permissions, and tool calls
- Document mitigations and retest high-risk controls
Deliverables
- LLM abuse case report
- Prompt, retrieval, and tool-risk findings
- Guardrail improvement playbook
- Monitoring and escalation recommendations
Who it is for
- Customer support chatbots
- AI agents
- Knowledge assistants
- Internal productivity copilots
Combine assessments into a focused security program.
Related services can be scoped together when the systems, risks, and timelines overlap.
AI security assessment for model exposure, prompt injection, data leakage, governance controls, and AI-assisted workflows.
API security testing for object-level access, authorization bypass, schema abuse, rate limits, and sensitive data exposure.
Deep application testing for authentication, authorization, business logic, data exposure, and OWASP-class risks.
Test an LLM System
Scope prompts, tools, retrieval, and chatbot roles for review.