LLM Security Assessment

SCS evaluates LLM-powered applications as deployed systems, not as isolated prompts. We test how identity, context, guardrails, logs, downstream rendering, and business workflows change the risk profile.

LLM Security Assessment

Secure Consulting Solutions (SCS) provides an LLM Security Assessment for applications that use large language models in chat, summarization, workflow, support, internal assistant, or product features.

The assessment validates whether deployed controls hold against prompt injection, system prompt exposure, data leakage, policy bypass, unsafe output handling, and role confusion.

Deliverables include confirmed findings, reproduction steps, captured model output, severity, remediation guidance, and retest cases.

Assessment summary
Identity Which users, roles, tenants, or workflows can reach sensitive model behavior or context.
Evidence Prompts, assistant output, hidden-context indicators, logs, rendered output, and workflow effects.
Risk Prompt injection, system prompt exposure, policy bypass, data exfiltration, and unsafe output handling.
Deliverable Executive summary, technical report, findings CSV, evidence bundle, remediation guidance, and retest plan.

What this assessment answers

  • Can users extract system prompts, hidden instructions, or sensitive context?
  • Can prompt injection alter application behavior or bypass intended policy boundaries?
  • Can the model disclose confidential data, secrets, logs, or conversation history?
  • Does output handling create XSS, injection, or unsafe downstream actions?
  • Are role, tenant, or workflow assumptions enforced outside the model?

What we test

  • Direct and indirect prompt injection
  • System prompt exposure
  • Policy bypass and role confusion
  • Data exfiltration through model output
  • Encoding and obfuscation attacks
  • Unsafe output rendering
  • Secrets and credential exposure
  • Conversation storage and logging risks

Evidence-first methodology

  • Architecture-specific target configuration
  • Declarative test suites selected by risk category
  • Raw output and latency capture
  • Evaluator-driven findings with severity and remediation
  • Manual evidence review before report delivery
  • Retest support for remediated controls

SCS Approach

Operator-led, evidence-first assessment.

LLM security testing is not a list of clever prompts. SCS validates whether the deployed application can be manipulated, whether the evidence supports the finding, and what engineering or governance change reduces risk.

Common Buyer Questions

What is an LLM Security Assessment?

It is a security test of an LLM-powered application as deployed, including identity, context, guardrails, output handling, logs, and downstream workflows.

Is this only prompt injection testing?

No. Prompt injection is one test area. SCS also validates data leakage, policy bypass, system prompt exposure, unsafe rendering, role confusion, and workflow impact.

Do you need source code?

Not always. Raw HTTP replay, header files, test accounts, and operator evidence can support many assessments. Code or architecture context improves root-cause analysis when available.

What evidence do you provide?

Findings include prompts, outputs, relevant context, observed behavior, severity, reproduction steps, remediation guidance, and retest cases.

Need evidence, not a generic scan?

SCS scopes AI security assessments around architecture, identities, data paths, evidence, remediation, and retest.