Overview
Large language models (LLMs) power critical features across products, from customer support to decision-making, but their flexibility also introduces new attack surfaces. Malicious inputs, prompt injection, or poorly isolated training data can make models reveal sensitive information, execute harmful instructions, or behave unpredictably.
Our LLM Penetration Testing service simulates real-world adversarial techniques against your model stack, including prompt injection, jailbreaks, data extraction, API abuse, and poisoning scenarios, to demonstrate how these weaknesses can lead to real security risks.
LLM
Scope Definition
Identify in-scope LLM endpoints, prompt templates, and connected data sources.
Prompt Injection Testing
Craft malicious inputs to override system instructions or extract hidden data.
Data Leakage Assessment
Test for unintended knowledge disclosure or proprietary data exposure.
Access Control Review
Validate authentication, authorization, rate limiting, and audit logging.
Output Validation
Detect harmful, biased, or insecure generated outputs and code generation flaws.
Integration Security
Assess API interfaces, middleware, and external connectors for misuse or pivoting.
Adversarial Robustness
Evaluate model behavior under fuzzing, red teaming, and adversarial prompt attacks.
Configuration Review
Review model parameters, logging policies, and data retention settings.
What you will get
Deliverables that keep every stakeholder aligned
We deliver more than raw findings: you receive a complete package that leadership, engineers, and auditors can all act on immediately.
Reporting package
Evidence-rich documentation for executives and technical teams.
Executive summary that explains business impact, key risks, and the
narrative behind the assessment.
Vulnerability summary with grouped issues, risk owners, severity,
and time-to-fix guidance.
Technical analysis that includes screenshots, observations, attack
flow notes, and proof-of-exploitation where appropriate.
Metrics & scoring covering CVSS, likelihood/impact rationales,
and remediation priority to unblock quick decisions.
Remediation toolkit
Clear next steps, mapped to the people fixing the issues.
Excel remediation tracker that consolidates every vulnerability with
owners, status, due dates, and comments so progress is easy to measure.
Prioritized backlog with quick wins, blocked items, and prerequisites
highlighted to reduce remediation friction.
Restitution meeting to walk through findings live, align on
fixes, and answer engineer questions while the context is fresh.
Optional retest to validate patches and refresh CVSS scores so the
final report reflects your latest posture.
Where this service excels
Recent scenarios our team solved
Real-world stories that mirror the way customers deploy, defend, and recover.
Support
Prompt injection through CRM notes
Inserted poisoned snippets that caused the assistant to exfiltrate internal ticket data.
Outcome
Added content sanitization, output filters, and system prompts enforcing data egress rules.
Productivity
Model override via indirect injection
Used external links to manipulate retrieval-augmented answers and spread malware links.
Outcome
Restricted external fetches, validated citations, and implemented allowlists for retrieval sources.
Financial
Toxic output in regulated workflows
Triggered biased loan advice when the model fell back to generic internet data.
Outcome
Bound outputs to vetted corpora, layered guardrails, and added human-in-the-loop checkpoints.
Testing Methodology
1
Scoping & Kick-off
Define in-scope endpoints, user roles, datasets, and test windows. Clarify objectives, success criteria, and escalation procedures.
2
Discovery & Mapping
Enumerate model prompts, system templates, connected APIs, and third-party integrations to understand the LLM’s operational landscape.
3
Prompt Injection & Manipulation
Test for prompt injection, jailbreaks, hidden prompt overrides, and instruction steering using controlled adversarial inputs.
4
Data Exposure Testing
Probe the model for sensitive data leakage, unintended memory retention, or exposure of proprietary knowledge.
5
Adversarial & Bias Testing
Conduct fuzzing, bias detection, and red-team simulations to evaluate robustness, ethical constraints, and context manipulation resilience.
6
Integration & API Security Checks
Assess input sanitization, authentication enforcement, rate limiting, and logging within model APIs and connected systems.
7
Reporting & Debrief
Deliver a comprehensive report with executive summary, scope, methodology, prioritized findings with PoCs, business impact, risk ratings, and remediation guidance, followed by a restitution meeting.
FAQ
Large language models are a new and rapidly evolving attack surface. Prompt injection, retrieval-based leakage, or insecure plugin integration can expose confidential data or enable lateral movement. Weak authentication and monitoring can lead to model abuse or silent data exfiltration. This assessment uncovers exploitable vectors across prompts, pipelines, tokenization quirks, vector databases, and connected integrations before adversaries do.
Typically between 3 and 7 business days, depending on model complexity, number of endpoints, and integration depth.
All testing is performed safely and non-destructively. For production environments, test windows are coordinated in advance to minimize risk. Potentially disruptive actions are executed only after explicit approval.