AI & LLM Security Testing
AI & LLM Security Testing
Securing the Systems That Power Your Business
AI systems introduce attack surfaces that traditional security tools do not cover. Prompt injection can cause an LLM to ignore safety guardrails, leak sensitive data, or execute unintended actions. Model theft techniques can extract proprietary model weights through repeated queries. Training data poisoning can corrupt model behaviour at inference time. SIRI Law LLP’s AI security practice assesses these risks — aligned with OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF.
Overview
AI & LLM Security Testing: Technical Depth Meets Legal Oversight
The rapid deployment of AI — in customer-facing products, internal tooling, and critical business processes — has created a new category of security risk that most organisations are not yet equipped to assess or defend against. Traditional penetration testing methodologies do not address AI-specific attack vectors.
SIRI Law LLP’s AI security team brings both technical AI expertise and legal advisory — uniquely positioned to assess AI security risk in the context of regulatory obligations (DPDPA, GDPR, emerging AI regulation) and to frame findings in terms that both technical and legal teams can act on.
Our assessments cover LLM-integrated applications, RAG pipelines, agentic AI systems, ML model APIs, and the cloud infrastructure that supports AI workloads. We assess against the OWASP LLM Top 10 and MITRE ATLAS — the leading frameworks for AI/ML security threats.
Legal + Technical AI Risk — One Integrated View
Our AI security assessments are unique in combining technical vulnerability findings with regulatory risk framing — mapping each finding to applicable data protection, AI liability, and sector-specific regulatory obligations.
This integrated view is increasingly required by enterprise procurement teams, regulators, and cyber insurers — making our AI security reports immediately useful to legal, compliance, and technical teams simultaneously.
Services Offered
What We Handle
- Prompt injection testing — direct and indirect injection attacks
- Jailbreaking and safety guardrail bypass assessment
- Sensitive data leakage through LLM output testing
- Model inversion and training data extraction attacks
- Model theft via API query extraction and model stealing
- Adversarial input attacks — evasion and robustness testing
- Training data poisoning assessment and data supply chain review
- RAG pipeline security — retrieval poisoning, context manipulation
- Agentic AI system security — tool misuse, privilege escalation via agent
- AI supply chain risk — third-party model and dataset security
- LLM plugin and tool integration security assessment
- Insecure output handling — XSS via LLM, code execution via output
- AI system access control and authorisation review
- Regulatory mapping — DPDPA, GDPR, OWASP LLM Top 10, NIST AI RMF
Client Benefits
Why Clients Choose SIRI Law LLP
OWASP LLM Top 10 Aligned
All assessments are structured against the OWASP LLM Top 10 — the industry-standard framework for AI application security, ensuring comprehensive and internationally recognised coverage.
Legal-Technical Integration
Findings are mapped to regulatory obligations — DPDPA, GDPR, emerging AI regulation — making our reports immediately actionable for legal and compliance teams as well as engineering.
Agentic AI Expertise
As AI agents become more prevalent, their security implications are more complex. We assess agentic systems — including tool use, planning, and multi-agent interaction — for novel attack surfaces.
Research-Backed Methodology
Our AI security methodology incorporates the latest academic and industry research on adversarial ML, LLM security, and AI red teaming — keeping pace with a rapidly evolving threat landscape.
HOW WE WORK
Our Engagement Process
01
Discovery & Scoping
We map your risk landscape, assets, and objectives before any engagement begins.
02
Engagement & Execution
Structured testing, investigation, or legal advisory delivered against agreed scope and timeline.
03
Findings & Reporting
Board-ready reports with risk-ranked findings, legal opinions, or evidence packages.
04
Remediation & Support
Technical fix verification, retesting, and legal implementation support at no extra scoping cost.
05
Ongoing Partnership
Retainer relationships with priority SLAs, proactive advisory, and quarterly strategic reviews.
Representative Matters
Typical Engagements
All matters described generically to protect client confidentiality.
LLM-Integrated Application Assessment
Identified a prompt injection vulnerability in a customer-facing AI chatbot — allowing an attacker to exfiltrate customer PII from the RAG context by crafting indirect injection payloads in content the bot was instructed to process.
AI Model Theft via API
Demonstrated model extraction from a proprietary ML model API — reconstructing a functional shadow model with 87% fidelity through 50,000 targeted inference queries within the platform’s rate limits.
Agentic AI Security Assessment
Assessed an AI agent with tool-calling capabilities — discovering privilege escalation through crafted tool outputs that caused the agent to execute actions outside its intended authorisation scope.
RAG Pipeline Security
Identified retrieval poisoning in a RAG-based enterprise knowledge system — demonstrating that an attacker with document upload access could manipulate the system’s responses to all users.
What to Expect
Client Outcomes
OWASP LLM Top 10 Report
Every finding mapped to the OWASP LLM Top 10 with technical proof-of-concept, business impact assessment, and remediation guidance.
Regulatory Risk Mapping
Findings mapped to DPDPA, GDPR, and applicable AI governance frameworks — so compliance teams understand the regulatory implications of each vulnerability.
Red Team Narrative
A narrative account of the AI red team engagement — showing how findings chain together into realistic attack scenarios against your AI systems.
Frequently Asked Questions
What is prompt injection and why is it dangerous?
Prompt injection is an attack where a malicious input causes an LLM to ignore its system instructions and execute attacker-controlled instructions instead. In applications where the LLM has access to data, tools, or the ability to take actions (send emails, execute code, query databases), prompt injection can lead to data exfiltration, unauthorised actions, and complete AI system compromise. It is currently the most critical vulnerability class for LLM-integrated applications.
Can you test our model even if we cannot share the model weights?
Yes. We conduct black-box assessments against your model API — without access to model weights, training data, or internal architecture. Black-box testing is sufficient to identify most practical attack vectors including prompt injection, model theft, jailbreaking, and data leakage. White-box assessments with model access provide additional depth, particularly for training data analysis.
How do AI security requirements affect our legal liability?
Under DPDPA and GDPR, organisations using AI to process personal data have accountability obligations — including demonstrating that AI systems are secure and that data subject rights (including erasure) can be fulfilled even for data used in AI training. An insecure AI system that leaks personal data through prompt injection or model inversion attacks creates direct regulatory liability. Our integrated assessments address both the technical risk and its legal implications.
Ready to Strengthen Your Security Posture?
We begin every engagement with a scoping call — no commitment required.
Also see: All Cybersecurity Services · Data Privacy Law