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.
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.
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.