AI Blindspot Category 7 of 8
Security & Privacy
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Blindspots in this category
Model Security Vulnerabilities
Occurs when AI models lack adequate protection against adversarial attacks, manipulation, or theft, potentially compromising system integrity, safety, and competitive advantage.
“What specific threats could compromise this AI model, and how have we tested our defences?”
Data Poisoning Attack Risks
Emerges when adversaries inject malicious examples into training or feedback data, causing the model to learn behaviours that benefit the attacker or undermine the system.
“How do we protect our AI training data from malicious manipulation?”
Privacy Leakage Through Model Outputs
Occurs when models memorise and reproduce sensitive items from their training data, leaking personal or confidential information through normal use.
“Could our AI system inadvertently reveal sensitive information about individuals in our training data?”
Infrastructure Security Gaps
Manifests when AI infrastructure lacks adequate security controls, creating vulnerabilities for compromise of models, data, and operations.
“Is the underlying infrastructure (servers, cloud services, networks) properly secured for AI workloads?”
Model Theft and Intellectual Property Risks
Occurs when proprietary models can be extracted, reverse-engineered, or copied through API queries or insider access, eliminating competitive advantage.
“How do we protect our AI models from theft or unauthorised copying?”
Incident Response Inadequacies
Manifests when organisations rely on generic incident response procedures inadequate for AI-specific threats, leading to prolonged exposure and ineffective containment.
“Do we have proper procedures to respond to AI security incidents?”
Recent cases in SEC
Long-Context Windows Enable Many-Shot Jailbreaking in Large Language Models
Recent case. Full summary visible to registered users — sign in to read.
General-Purpose AI Persuasion Capabilities Enable Large-Scale Manipulation
Recent case. Full summary visible to registered users — sign in to read.
General-Purpose AI Systems Gaming Their Own Evaluations
Recent case. Full summary visible to registered users — sign in to read.
AI Self-Proliferation: Autonomous Copying and Resource Acquisition Risk
Recent case. Full summary visible to registered users — sign in to read.
Biased AI Weaponised at Scale to Manipulate Populations and Critical Infrastructure
Recent case. Full summary visible to registered users — sign in to read.
Backdoor Attacks Embedded in General-Purpose AI Models During Training
Recent case. Full summary visible to registered users — sign in to read.
Test your organisation against SEC
The Velinor AI Audit maps your AI portfolio against every blindspot in this category and benchmarks against documented sector failures.