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Cyber Security for AI Developers and AI Startups: Secure Your Models, Protect Training Data, Ship with Integrity

AI systems introduce attack surfaces that traditional security tools cannot see. Standard vulnerability scanners cannot detect prompt injection, training data poisoning, or model extraction. Model weights and training data are the business valuation, and enterprise CISOs will not buy AI products without evidence of adversarial testing and compliance. The EU AI Act GPAI obligations are live, OWASP LLM Top 10 defines the AI threat landscape, and ISO/IEC 42001 is emerging as the governance standard enterprise buyers demand.

  • CREST accredited LLM security assessment and AI red teaming across OWASP LLM Top 10
  • AI API penetration testing for prompt abuse, model extraction, and data leakage through outputs
  • EU AI Act compliance, CE+ and ICA certification, and ISO/IEC 42001 readiness
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Why AI Developers Cannot Rely on Standard Application Security

  • Traditional software relies on deterministic logic. LLMs are probabilistic, meaning inputs and outputs are unpredictable, requiring adversarial testing that automated scanning cannot deliver
  • Prompt injection (direct and indirect) manipulates AI behaviour in ways absent from every traditional security framework
  • Training datasets and model weights are tier-one IP targeted by nation-state actors in the global AI race, yet most AI teams store them in cloud buckets with excessive permissions
  • A single compromised model propagates harm across every downstream customer, partner, and integrated application
  • RAG architectures connecting LLMs to enterprise data through vector databases create RBAC bypass risk where the AI summarises confidential documents for unauthorised users
  • EU AI Act GPAI obligations carry fines up to 3% of global turnover for non-compliance, creating urgency for AI teams selling into UK and European markets
AI DEVELOPER SECURITY SPECIALISMS
LLM Security and OWASP LLM Top 10
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AI Red Teaming and Adversarial Testing
ML Pipeline and Training Infrastructure
3
4
RAG and Vector Database Security
EU AI Act and ISO/IEC 42001 Compliance
5

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Why AI Developers and AI Startups Choose Cyphere

Applied GenAI and Wrapper Applications
Startups building user-facing apps on foundation APIs (OpenAI, Anthropic, Gemini). Extreme vulnerability to prompt injection, jailbreaking, and insecure output handling where the AI generates malicious payloads rendered in user browsers. System prompt leakage exposing proprietary instructions. Input validation and output sanitisation assessment. Rate limiting and abuse prevention on AI-powered endpoints.
Enterprise RAG and Vector Database Providers
AI search and knowledge assistants connected to enterprise data using vector databases (Pinecone, Weaviate, Milvus). RBAC bypass where the AI is tricked into summarising confidential HR documents for unauthorised employees. Retrieval logic assessment ensuring strict access control enforcement before context is returned. Tenant isolation in multi-customer RAG deployments.
Autonomous AI Agents and Agentic Workflows
AI systems designed to execute code, send emails, and trigger APIs autonomously. SSRF and privilege escalation risk if agent permission boundaries are breached. Tool-use validation ensuring agents cannot exceed authorised actions. Guardrail assessment for autonomous decision-making systems. Agentic workflow security architecture review.
Model Fine-Tuners and Open-Source AI Builders
Teams downloading open-source models from Hugging Face and fine-tuning on proprietary datasets. Supply chain poisoning through malicious tensor files or Pickle exploits. Model inversion attacks where competitors extract proprietary training data from fine-tuned models. Dependency validation and repository integrity checks.
MLOps and AI Infrastructure Platforms
Model registries, data labelling tools, and pipeline orchestrators. Ultimate supply chain targets where breach poisons training pipelines of thousands of downstream customers. GPU cluster infrastructure security. Training environment access controls. Secrets management across ML pipelines.
DeepTech and Foundation Model Builders
Teams training base models from scratch on massive GPU clusters. Model weights as tier-one IP requiring national-security-grade protection. Cloud and on-premise GPU infrastructure security. Training data provenance and integrity. Compute environment isolation and access management.

Why Trust Cyphere with Your AI Security?

01CREST-Accredited
Testing
02OWASP
LLM Expertise
03AI
Red Teaming
04CE+
Certification Body
05ICA
Certification Body
06EU
AI Act Knowledge
07AI
Sector Record

Cyber Essentials Plus Certification for enterprise AI procurement

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The Threats Unique to AI Developers That General Security Misses

Prompt Injection and Jailbreaking
Training Data Poisoning and Dataset Manipulation
Model Extraction and Inversion
Supply Chain Attacks via Malicious Models and Packages
RAG Data Leakage and RBAC Bypass
Autonomous Agent Exploitation and Privilege Escalation
01

Prompt Injection and Jailbreaking

Direct prompt injection overriding system instructions. Indirect injection hiding malicious prompts in documents the AI processes. Jailbreaking bypassing safety filters. System prompt extraction exposing proprietary instructions. Insecure output handling where LLM-generated payloads exploit backend systems.

02

Training Data Poisoning and Dataset Manipulation

Attackers subtly altering public datasets causing biased or compromised outputs. Poisoned fine-tuning data degrading model integrity. Backdoor insertion during training. Data provenance failures allowing untrusted sources into pipelines.

03

Model Extraction and Inversion

Competitors querying APIs thousands of times to reverse-engineer proprietary model weights. Model inversion extracting sensitive PII from training data. Intellectual property theft through systematic output analysis. Distillation attacks replicating model capabilities.

04

Supply Chain Attacks via Malicious Models and Packages

Malicious PyTorch and Pickle files executing remote code on download from Hugging Face. Compromised Python packages in ML dependencies. Tampered model weights in third-party registries. Upstream model vulnerabilities propagating downstream.

05

RAG Data Leakage and RBAC Bypass

Vector database retrieval ignoring user permissions. AI summarising confidential documents for unauthorised users. Cross-tenant data exposure in multi-customer RAG. Embedding-level data leakage.

06

Autonomous Agent Exploitation and Privilege Escalation

Agentic workflows exceeding permission boundaries. SSRF through tool-use APIs. Agents executing unauthorised code or accessing restricted systems. Guardrail bypass in autonomous decision chains.

Navigating AI Regulatory Complexity

AI developers face emerging AI-specific regulation alongside existing data protection and enterprise procurement requirements. Compliance is the primary enabler for selling AI products into UK and European enterprise markets.
01

EU AI Act (GPAI Obligations)

Risk classification, technical documentation, and adversarial testing for general purpose AI

02

OWASP LLM Top 10

Industry standard framework for LLM vulnerability assessment

03

ISO/IEC 42001

AI management system standard for enterprise governance

04

UK GDPR and DPA 2018

AI-processed personal data, automated decision-making, and data subject rights

05

Cyber Essentials Plus

Enterprise and public sector procurement baseline for AI vendors

06

IASME Cyber Assurance (ICA)

Comprehensive resilience standard for AI companies

07

ICO AI and Data Protection Guidance

UK-specific AI fairness, transparency, and accountability

08

FCA AI Regulations

Where AI is deployed in financial services applications

09

Cyber Security and Resilience Bill

Upcoming legislation for digital service providers including AI

10

PCI DSS v4.0

Where AI platforms process payment data

Cyphere's AI Developer Security Projects

LLM Security Assessment and AI Red Teaming

OWASP LLM Top 10 assessment covering prompt injection, jailbreaking, insecure output handling, and system prompt extraction. Structured adversarial testing of deployed models under real-world attack conditions.

AI API Penetration Testing

API testing for prompt abuse, model extraction, rate-limit bypass, and data leakage through outputs. Authentication and authorisation for AI endpoints. Webhook and callback security.

RAG, Vector Database, and Architecture Security

Retrieval logic assessment ensuring RBAC enforcement. Vector database access controls. Tenant isolation in multi-customer RAG. Embedding-level data leakage testing. Security architecture review.

ML Pipeline and Cloud Infrastructure Security

Training environment access management. GPU cluster security. Data ingestion controls. Secrets management across pipelines. AWS, Azure, and GCP configuration review for AI workloads.

Model Supply Chain and Dependency Security

Open-source model integrity checks. Hugging Face repository validation. Python dependency analysis. Malicious package detection. Training data provenance assessment.

EU AI Act, CE+, and Compliance Readiness

EU AI Act GPAI risk classification and technical documentation. ISO/IEC 42001 gap analysis. CE+ and ICA certification as authorised body. UK GDPR for AI-processed data.

AI Developer Security Challenges

Prompt Injection, Jailbreaking, and LLM Exploitation

Training Data Poisoning and Model Integrity

Model Extraction, Inversion, and IP Theft

RAG Data Leakage and RBAC Bypass

AI Supply Chain and Open-Source Model Risk

EU AI Act, ISO/IEC 42001, and Enterprise Compliance

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Key Cyber Security Areas for AI Developers

Cyphere’s AI experience spans GenAI applications, enterprise RAG, autonomous agents, model fine-tuners, MLOps platforms, and foundation model builders covering LLM security, AI red teaming, and compliance.
  • LLM Security and OWASP LLM Top 10 — Prompt injection, jailbreaking, insecure output handling, system prompt extraction, and safety filter assessment.
  • AI Red Teaming — Structured adversarial testing of deployed models. Boundary constraint testing. Real-world attack simulation.
  • RAG and Vector Database Security — RBAC enforcement, retrieval logic, tenant isolation, and embedding-level data leakage assessment.
  • ML Pipeline and Training Infrastructure — GPU cluster security, data ingestion, secrets management, and training environment access controls.
  • Cyber Essentials Plus and ICA Certification — Authorised CE+ and ICA body. Enterprise AI procurement. Public sector eligibility.
  • EU AI Act and ISO/IEC 42001 — GPAI risk classification, technical documentation, adversarial testing obligations, and governance readiness.

Cyber security compliance guidance for AI developers

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Frequently Asked Questions

What makes AI security fundamentally different from traditional application security?
Traditional software uses deterministic logic. LLMs are probabilistic with unpredictable inputs and outputs, requiring adversarial red teaming to find edge cases that automated scanners cannot detect.
How do you test for prompt injection in LLM-powered products?
We conduct adversarial testing covering direct injection, indirect injection through processed documents, system prompt extraction, and jailbreaking. Testing validates input constraints and output sanitisation controls.
How do you protect training data and model weights from theft?
We audit cloud infrastructure, enforce strict IAM on storage, and secure MLOps pipelines against unauthorised access. Model weights and training data receive tier-one asset protection assessment.
What do EU AI Act GPAI obligations mean for AI developers?
GPAI model providers must maintain technical documentation, perform adversarial testing, and report serious incidents. Non-compliance carries fines up to 3% of global turnover.
How do you secure RAG architectures and prevent vector database leakage?
We review retrieval logic ensuring the AI enforces RBAC before returning context. Testing covers cross-tenant exposure, embedding-level leakage, and permission bypass through crafted prompts.
What is AI red teaming and how does it differ from penetration testing?
Penetration testing targets network and code vulnerabilities. AI red teaming targets the model's logic, attempting to bypass safety filters, extract training data, and manipulate autonomous behaviour.
How do you validate security of open-source models from Hugging Face?
We perform supply chain assessments validating downloaded weight integrity, checking for malicious Pickle exploits, and testing for known vulnerabilities in upstream model architectures.
How do you secure ML pipelines from data ingestion through deployment?
We conduct security architecture reviews of training environments covering secrets management, infrastructure segmentation, access controls, and data provenance from annotation to model serving.
Can Cyphere support ISO/IEC 42001 and CE+ for enterprise AI sales?
Yes, we deliver ISO/IEC 42001 gap analysis proving AI governance maturity alongside CE+ and ICA certification as an authorised body for enterprise and public sector procurement.
How often should AI teams run security assessments?
Deep red teaming before every major model release. Annual CREST accredited infrastructure testing. Immediate assessment after significant architecture changes, new model deployments, or dataset updates.

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