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15/08/2026 6:12 pm
TL;DR: Adversarial prompt engineering manipulates LLM inputs to bypass safety controls, extract sensitive information, and trigger unintended outputs, with Obsidian Security describing prompt injection, jailbreaking, and multi-turn manipulation as the main attack paths. The risk is now operationally relevant for enterprise AI governance because weak identity controls, poor monitoring, and unmanaged dependencies leave AI systems easier to manipulate than many teams assume.
NHIMG editorial — based on content published by Obsidian Security: Adversarial Prompt Engineering: The Dark Art of Manipulating LLMs
By the numbers:
- 17 minutes, redentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: How should security teams stop prompt injection from turning into tool misuse?
A: They should enforce authorization at the tool or resource boundary, not inside the model.
Q: Why do LLMs create risk in identity and access management?
A: LLMs create risk when teams confuse fluent answers with verified security evidence.
Q: What do organisations get wrong about protecting AI assistants from malicious prompts?
A: They often focus on filtering bad language while ignoring the authorisation layer around the model.
Practitioner guidance
- Define model tool boundaries as identity boundaries Inventory every LLM connector, plugin, and API integration, then assign a scoped workload identity to each one with the minimum permissions needed for the task.
- Separate untrusted input from trusted instructions Implement strict prompt segmentation so user text, retrieved content, and system policy are handled differently.
- Log every sensitive model action Capture prompts, retrieved documents, tool calls, and resulting outputs in an audit trail that security and compliance teams can review.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Walkthroughs of specific prompt injection and jailbreak patterns that defenders can reproduce in testing
- Step-by-step mitigation methods for input validation, output filtering, and behavioural monitoring
- Implementation guidance for zero-trust controls around AI tool access and audit logging
- Operational examples showing how teams can integrate AI incident response into existing SOC workflows
👉 Read Obsidian Security's analysis of adversarial prompt engineering and LLM attack paths →
Adversarial prompt engineering: are your AI controls keeping up?
Explore further
15/08/2026 6:19 pm
Prompt injection is now an authorisation problem, not just a content moderation problem. Once a model can access tools, records, or workflows, the question is no longer whether it generates unsafe text. The real issue is whether untrusted language can trigger privileged behaviour. That shifts the governance burden toward IAM, logging, and tool-scoped controls, because the model's output may become an indirect control signal for downstream systems.
A question worth separating out:
Q: Who is accountable when an AI model exposes data after a prompt attack?
A: Accountability usually sits with the team that approved the model's access model, the data owners who exposed the content, and the security function that failed to monitor the workflow. Frameworks such as NIST AI RMF and identity governance practices help define ownership, but the organisation must make tool access, logging, and review responsibilities explicit.
👉 Read our full editorial: Adversarial prompt engineering exposes the AI security gap in LLMs
