Notifications
Clear all
Topic starter
20/08/2026 5:51 pm
TL;DR: GenAI systems are being attacked through prompt injection, contextual exploits, obfuscation, and multimodal evasion, according to ActiveFence’s taxonomy of real-world red team findings. Static filters alone do not withstand adversarial input chains, and security programmes need continuous testing, scenario planning, and governance that treats AI systems as a live attack surface.
NHIMG editorial — based on content published by ActiveFence: Exposing the Threat Landscape, a taxonomy of GenAI attack vectors
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: How should security teams test GenAI systems for prompt injection?
A: Test the full path, not just the chat box.
Q: Why do AI agents create new privilege risk for enterprises?
A: AI agents can chain actions across tools, inherit delegated access, and execute at machine speed without a person confirming each step.
Q: What do organisations get wrong about AI safety and access control?
A: Organisations often focus on model outputs while ignoring the privileges behind the model.
Practitioner guidance
- Define untrusted-input boundaries for every model entry point Map where prompts, documents, images, metadata, and session memory enter the system, then separate user content from system instructions and tool policies.
- Scope tool access to the minimum action set Restrict each AI workflow to the smallest possible tool and data set, and require explicit approval for anything that can write, delete, or exfiltrate.
- Test multimodal and encoded abuse paths Include Base64, homoglyphs, zero-width characters, PDFs, screenshots, and other transformed inputs in red-team scenarios so the team can see what the model really processes.
What's in the full article
ActiveFence's full blog covers the operational detail this post intentionally leaves for the source:
- Expanded examples of direct and indirect prompt injection patterns across chat, files, and web content
- Red teaming workflow guidance for fuzzing, multilingual evasion, and chained multi-turn attacks
- Practical mitigation steps for content channels, moderation layers, and human-in-the-loop review
- Examples of adversarial testing methods used to simulate real attacker behaviour in GenAI deployments
👉 Read ActiveFence's taxonomy of GenAI attack vectors and red team methods →
GenAI attack vectors: are static guardrails keeping up?
Explore further
20/08/2026 6:30 pm
Static guardrails are not a control strategy for GenAI. The article’s taxonomy shows why attacker behaviour changes faster than rule sets do, especially when input can arrive through documents, images, memory, or encoded text. That makes AI security a continuous verification problem, not a one-time moderation problem. For practitioners, the practical conclusion is that safety policy must be tested against live abuse paths, not only reviewed on paper.
A question worth separating out:
Q: How should teams reduce the blast radius of AI coding agents in production-adjacent systems?
A: Teams should restrict agent credentials to the smallest possible scope, separate staging from production authority, and keep backups outside the same writable boundary as live data. They should also require out-of-band approval for destructive operations. That combination limits damage even when an agent makes a bad decision.
👉 Read our full editorial: GenAI attack vectors expose the limits of static AI guardrails
