AI & LLM Security Testing

As organizations increasingly deploy large language models (LLMs), ensuring their security becomes critical. P.I.V.O.T offers AI & LLM, OWASP Top 10 Security Testing to uncover vulnerabilities such as prompt injection, data leakage, and misuse, helping teams safely adopt and govern generative AI systems across cloud and endpoint environments.

24h

Response SLA

27001

ISO Certified

AI & LLM Security Testing

aiLlmSecurityTesting

Overview

What is AI & LLM Security Testing?

AI & LLM Security Testing involves evaluating the behavior and resilience of large language models against adversarial inputs, data extraction attempts, and model abuse. This proactive testing ensures ethical AI usage, protects against misuse, and helps comply with AI governance frameworks.

Prompt Injection & Jailbreak Defense

Identify how LLMs can be manipulated to bypass filters or leak internal logic using adversarial prompts, and deploy hardening techniques.

Abuse & Misuse Detection

Test your models against scenarios involving hate speech, misinformation, or cyber abuse to ensure safe and responsible deployment.

Model Governance & Compliance

Ensure your AI deployment aligns with internal governance and external standards by auditing outputs, usage, and training data handling.

Capabilities

What we uncover.

Real vulnerabilities — mapped to your threat landscape, not a generic checklist.

01

Prompt Injection Testing

Simulate prompt injections to test if malicious actors can override instructions, access hidden functions, or extract training data.

Key Areas

  • Jailbreak scenario testing
  • Prompt override detection
  • Behavioral guardrail audits
  • Mitigation strategy recommendations
02

Access Management for AI APIs

Secure LLM endpoints by implementing strict authentication, logging, and abuse throttling, protecting APIs from unauthorized use.

Key Areas

  • API key misuse detection
  • Rate limiting & abuse control
  • Role-based permissions
  • Access audit logging
03

Output Control & Red Teaming

Analyze model responses across various inputs to detect hallucinations, inappropriate replies, or unsafe content generation.

Key Areas

  • Toxicity testing
  • Bias and fairness audits
  • Red-teaming adversarial inputs
  • Hallucination detection
04

LLM Data Leakage Testing

Evaluate if models are revealing training data or sensitive information inadvertently, especially in fine-tuned or internal deployments.

Key Areas

  • PII exposure detection
  • Memorization evaluation
  • Training data recall tests
  • Data governance validation
05

Incident Simulation for AI Misuse

Run breach scenarios involving AI misuse to assess readiness and define response protocols for governance and legal compliance.

Key Areas

  • Simulated misuse incidents
  • Root cause analysis
  • Incident documentation
  • Response plan development
06

Reporting & Governance Guidance

Receive detailed AI security reports and recommendations to enhance policy enforcement, risk posture, and audit-readiness.

Key Areas

  • Risk scoring & model audit
  • Policy gap analysis
  • Responsible AI guidance
  • Governance reporting packs

Ready to scope

Ready to test your AI models?

Secure your AI pipelines with expert prompt testing and LLM threat modeling. Talk to us today.

How We Work

Our Methodology

A systematic, repeatable process — from first call to final remediation.

01

Consultation & Scoping

We collaborate closely with your team to understand your environment, define objectives, and tailor simulations to the threats most relevant to your business.

02

Threat Modeling & Risk Analysis

Our experts map attack surfaces and model realistic adversary behaviour, identifying the highest-impact risks before any testing begins.

03

Vulnerability Identification

Our red team operates like real attackers — probing your defenses, chaining exploits, and surfacing weaknesses you didn't know existed.

04

Reporting & Remediation

You receive a clear, prioritised report: executive summary for leadership, technical findings for engineers, and a remediation roadmap for both.

05

Post-Engagement Support

We stay engaged after delivery — answering questions, validating fixes, and helping your team build security muscle for the long term.

Client Testimonials

Trusted by Security Teams

Frequently Asked Questions

What kinds of AI models can be tested?

We test both publicly available LLMs (e.g., OpenAI, Claude) and proprietary/fine-tuned models hosted on-prem or in private cloud environments.

Can this testing detect if models are leaking training data?

Yes, our testing simulates PII probes and memorization attacks to determine if your models are revealing sensitive training data unintentionally.

Does this work with RAG pipelines or fine-tuned models?

Absolutely. We evaluate full LLM stacks, including retrieval-augmented generation (RAG), embeddings, and custom fine-tunes for leakage and abuse risk.

How often should LLM security testing be done?

We recommend testing before production deployment and then periodically with each update or data/train revision to maintain secure AI operations.