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
How SCS tests LLM applications.
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.
What an LLM security testing report includes.
Buyers routinely ask what they will actually receive before they engage a vendor. Every SCS LLM assessment produces the same evidence package, so the report can be handed to engineering, security leadership, or an auditor without translation.
Report contents
| Executive summary | Business-level statement of what was tested, what held, and what did not. |
| Technical findings | Each finding with severity, affected component, and root cause rather than a generic category label. |
| Reproduction steps | The exact prompt sequence, account, role, and context required to reproduce the behavior. |
| Captured evidence | Raw model output, hidden-context indicators, rendered output, and relevant log entries. |
| Findings CSV | Machine-readable export for ticketing, tracking, and remediation workflows. |
| Remediation guidance | The engineering or governance change that closes the issue, not a restatement of the finding. |
| Retest cases | Named test cases so a fix can be verified after remediation. |
Why evidence matters more than volume
Automated LLM scanners tend to produce long lists of unvalidated "potential" issues. That output is difficult to act on, because an engineering team cannot tell which entries reflect real, reachable behavior in the deployed system.
SCS reviews every finding manually before delivery. A finding appears in the report only when an operator reproduced the behavior against the deployed application and captured the output. Items that could not be reproduced are excluded rather than reported as theoretical risk.
The practical result is a shorter report that engineering can work through directly, with each entry carrying the evidence needed to confirm it and the retest case needed to close it.
LLM assessment 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.
Can SCS test an internal assistant built on Microsoft Copilot or a similar platform?
Yes. Internal assistants are a common assessment target, whether built on Microsoft 365 Copilot, a hosted model API, or an in-house orchestration layer. For Copilot specifically, SCS also offers the Microsoft 365 Copilot Exposure Assessment, which focuses on what representative user roles can discover, summarize, and cite through the tenant.
How is this different from an automated LLM vulnerability scan?
A scanner submits a fixed payload list and reports what looks anomalous, without confirming whether the behavior is reachable in your deployment. SCS reproduces each finding against the deployed application, captures the output, and discards what cannot be reproduced. The report reflects confirmed behavior rather than potential behavior.
Can you identify LLM data exfiltration paths?
Yes. Data exfiltration testing covers what the model can be induced to disclose from system context, retrieved documents, conversation history, connected tools, and logs, including paths that require indirect injection through content the model ingests rather than direct user input.
How should we evaluate an LLM security testing vendor?
Ask for a redacted sample finding before you engage. A useful finding shows the reproduction steps, the captured model output, the affected component, and a specific remediation. If a vendor cannot show reproducible evidence for a past finding, the engagement is likely to produce a list of categories rather than actionable results.