LLM Security Testing Services
Test large language model security across prompt injection, jailbreaks, sensitive data leakage, guardrails, logging and human review. Large Language Models are now used in copilots, chatbots, customer support, document automation, development workflows, security operations, GRC platforms and internal knowledge systems. These systems can create business value, but they also introduce new risks when model behavior, prompts, outputs, integrations and data handling are not tested properly.
ServQual helps organizations test LLM security in a structured, controlled and evidence-ready way. Our LLM Security Testing Services assess how AI applications respond to unsafe instructions, adversarial prompts, sensitive data exposure, weak guardrails, unreliable outputs and insufficient human review.
Why LLM Security Testing Matters
LLM applications behave differently from traditional software. They may follow user instructions, retrieve enterprise context, summarize sensitive information, call tools, generate business outputs and interact with workflows. This makes security testing more complex.
A weak LLM implementation can create risks such as:
Prompt injection
Jailbreak attempts
Sensitive data leakage
Lack of human review
Weak guardrails
Poor prompt logging
Unsupported or misleading outputs
Poor mapping to AI security and governance controls
Over-reliance on AI-generated answers
Privacy and compliance exposure
Unsafe tool or workflow actions
LLM Security Testing helps organizations validate whether AI systems behave safely under adversarial, unexpected and business-critical conditions.
What ServQual Helps With
Test whether the LLM can be manipulated by direct or indirect instructions that attempt to override system prompts, bypass controls or disclose restricted information.
Review whether the LLM exposes personal data, customer data, confidential documents, credentials, secrets, internal instructions or restricted business information.
Assess whether prompts, responses, user actions, tool calls and error events are logged with enough detail for investigation, auditability and incident response.
Assess whether LLM-connected tools, plugins, APIs or agent workflows can be misused to perform unauthorized actions, retrieve restricted data or trigger unsafe automation.
Map testing areas and findings to OWASP LLM Top 10 themes where relevant, supporting structured remediation and security reporting.
Assess whether users can bypass safety rules, operating boundaries, policies or response restrictions using adversarial prompts or multi-step interaction patterns.
Validate whether input filters, output filters, policy controls, refusal logic, content boundaries and workflow restrictions operate effectively.
Review whether high-impact AI outputs are routed to human review, approval or escalation before being used in business, compliance, legal or customer-facing workflows.
Review whether the LLM produces unsupported claims, incorrect compliance statements, misleading recommendations or outputs that cannot be traced to approved sources.
Create a prioritized remediation plan covering prompt controls, guardrails, data leakage, logging, human review, tool access, source grounding and governance improvements.
Key LLM Security Risks We Test
A user attempts to manipulate the LLM directly through the chat interface or prompt input.
The LLM processes untrusted content from documents, websites, emails, tickets or retrieved sources that contain hidden or malicious instructions.
A user attempts to bypass model restrictions or operating boundaries through adversarial wording, roleplay, encoding or multi-turn prompt patterns.
The system reveals sensitive information, internal instructions, restricted source content, credentials, personal data or confidential business data.
The LLM lacks effective input validation, output filtering, refusal logic, approval workflows or response boundaries.
The LLM can call tools, APIs or workflow actions without sufficient authorization, validation, confirmation or logging.
Security teams cannot investigate what prompt was used, what content was retrieved, what answer was generated or which tool actions were triggered.
High-impact AI outputs are used without human validation, creating risk in compliance, legal, privacy, customer communication or security workflows.
LLM Security Testing Approach
Define the LLM application, business use case, user roles, data sources, integrations, model boundaries, tool permissions and testing objectives.
Identify likely misuse paths, adversarial prompt patterns, data exposure risks, tool abuse paths and compliance-impacting output risks.
Run controlled tests across prompt injection, jailbreaks, sensitive data leakage, guardrails, logging, human review and tool or agent actions.
Check whether controls block, detect, log, escalate or safely handle unsafe inputs, unsafe outputs and high-risk actions.
Map findings to AI security controls, governance requirements and OWASP LLM Top 10 themes where relevant.
Provide findings, evidence, risk severity, affected workflows, remediation recommendations and executive-ready summary.
Support remediation planning across prompt design, guardrails, access control, logging, monitoring, human review and source governance.
Example Assessment Areas
Prompt injection testing
Jailbreak testing
Sensitive data leakage testing
Guardrails and refusal behavior
Prompt and response logging
Compliance statement validation
Tool and API action control
RAG-connected LLM behavior
Unauthorized retrieval attempts
Unsafe summarization of sensitive documents
Output hallucination and unsupported claims
Human review and approval workflow
Data minimization and privacy handling
Abuse case testing for enterprise workflows
OWASP LLM Top 10 mapping
How SUSAN Supports LLM Security Governance
SUSAN supports AI security governance by helping teams track risk findings, control gaps, remediation actions and compliance visibility.
With SUSAN, teams can:
Track LLM security risks and remediation actions
Connect findings to AI Risk Scoring
Map AI security issues to controls and governance requirements
Maintain visibility through a Unified GRC Dashboard
Support Continuous Monitoring & Evidence
Improve audit readiness for AI security and compliance reviews
Connect LLM risk with broader cybersecurity, privacy and GRC workflows
Business Outcomes
Clear understanding of LLM security exposure
Reduced prompt injection and jailbreak risk
Stronger guardrails and workflow controls
Better sensitive data leakage prevention
Improved logging and investigation readiness
Stronger human review for high-impact AI outputs
Better alignment with AI governance and compliance expectations
Practical remediation roadmap for secure LLM adoption
Better evidence for leadership, audit and customer assurance
Who Needs This Service?
This service is suitable for:
Organizations deploying enterprise LLM applications
SaaS companies embedding AI assistants or copilots
Security teams testing AI application resilience
Privacy teams assessing data leakage exposure
GRC teams reviewing AI governance controls
Product teams building AI-enabled workflows
SOC teams using AI in investigation or alert triage
Legal and compliance teams reviewing AI-generated outputs
Businesses using chatbots, copilots, agents or RAG-connected LLMs
FAQ
Most frequent questions and answers
LLM Security Testing assesses whether large language model applications are exposed to prompt injection, jailbreaks, sensitive data leakage, weak guardrails, unsafe tool use, poor logging or insufficient human review.
LLM security testing matters because AI systems can process sensitive data, generate business outputs, retrieve enterprise context, call tools and influence workflows. Weak controls can create security, privacy and compliance risk.
Prompt injection testing checks whether users or untrusted content can manipulate the LLM into ignoring instructions, bypassing controls, revealing restricted data or producing unsafe outputs.
Jailbreak testing checks whether adversarial prompts can bypass model rules, safety boundaries, content restrictions or operating instructions.
Yes. Testing includes checks for exposure of personal data, confidential business data, credentials, secrets, internal prompts, restricted documents and other sensitive information.
Yes. Guardrails testing reviews input filters, output filters, refusal logic, policy controls, human approval workflows and tool action restrictions.
Prompt logging records user prompts, AI responses, tool calls, retrieval events and errors so security and compliance teams can investigate AI behavior and support auditability.
Findings can be mapped to OWASP LLM Top 10 themes where relevant, helping teams structure risk reporting and remediation planning.
AI Security Assessment is broader and covers AI inventory, governance, vendor risk, data leakage and compliance readiness. LLM Security Testing focuses on testing LLM application behavior and technical security controls.
RAG Security Assessment focuses on retrieval pipelines, vector databases, source governance and retrieval permissions. LLM Security Testing focuses on model behavior, prompts, jailbreaks, guardrails, logging and sensitive data leakage.
Start Your LLM Security Test
Validate your LLM before prompt injection, data leakage, weak guardrails or unsafe automation becomes a security or compliance issue.
ServQual helps organizations test LLM applications, strengthen AI controls and build evidence-ready AI security governance.
