NCSC warning: Prompt‑injection may never go away — harden your AI now

NCSC warns prompt‑injection attacks may never go away — here’s why your AI plans need a reality check

In the past 24 hours a senior technologist at the U.K.’s National Cyber Security Centre (NCSC) warned there is “a good chance” prompt‑injection attacks against generative AI will never be eliminated. The implication is stark: as organisations rush to embed large language models and other generative AI into workflows, they are inviting a new, persistent class of attack that can subvert models, trick systems into revealing secrets, or cause automated processes to behave dangerously.

Prompt injection is not the stuff of sci‑fi; it is a pragmatic, practical technique whereby an adversary crafts input to a model or its surrounding systems in order to override instructions, exfiltrate data, or trigger unsafe actions. The NCSC’s frank assessment means this is no longer a theoretical paternal‑warning — it’s a living risk that needs to be managed as part of your information security and continuity programmes.

Who is impacted — and why businesses should care now

Every organisation that uses generative AI in any operational capacity is at some level exposed. That includes vendors offering AI‑enhanced SaaS, internal automation that ingests user content, customer‑facing chatbots, and tools that generate code, configuration or legal text.

Why it matters to businesses:

• Data exposure: poorly constrained prompts can cause models to disclose sensitive training data, customer information or system secrets.

• Process compromise: automated workflows that act on model outputs can be instructed to perform unauthorised changes, creating operational risk.

• Regulatory and contractual risk: data breaches or uncontrolled data processing may breach legal obligations, regulatory requirements or third‑party contracts.

• Reputational damage: incorrect, biased or maliciously generated outputs can harm brand trust and customer safety.

The key point is this: prompt injection attacks exploit the gap between “what the model is told to do” and “what the rest of your estate expects it to do”. If you’ve let AI touch anything that affects confidentiality, integrity or availability, you have a new threat surface to manage.

What could happen if organisations ignore this warning

Ignoring the NCSC warning is not merely complacent — it’s dangerous. Examples of realistic outcomes include unauthorised disclosure of intellectual property, fraudulent transactions triggered by malicious prompts, or automated decision systems taking harmful actions because their instruction set has been poisoned. Even where harms are limited, the accumulation of small failures can erode customer trust and invite regulatory scrutiny.

Crucially, the NCSC’s position that prompt‑injection may be persistent means residual risk will remain even with good engineering. That makes strong detection, containment and response capabilities essential — not optional extras.

Practical, ISO‑aligned steps to reduce risk (and sleep better at night)

ISO 27001 provides a tested structure to manage new technical risks like prompt injection, while ISO 22301 helps ensure business continuity if an AI‑driven process misbehaves. Below are concrete measures that align to common ISO clauses and to good security practice.

Governance and risk management (ISO 27001: context, leadership, risk assessment)

• Include AI components in your asset inventory and risk assessment. Treat models, prompt templates, APIs and training data as information assets.

• Update risk registers to reflect prompt‑injection threats and ensure leadership signs off on residual risk and mitigation plans.

Secure development and deployment (ISO 27001 A.14)

• Apply threat modelling and adversarial testing to prompt pipelines — red‑team prompts as well as black‑box fuzzing.

• Harden integration points: use API gateways, input validation, allow‑listing, and instruction wrapping to prevent malicious content reaching models or downstream systems.

• Constrain outputs for high‑risk operations. For example, require human confirmation for actions that change production state, move money, or release data.

Access control and data handling (ISO 27001 A.9, A.8)

• Minimise sensitive data sent to models. Use tokenisation, data minimisation and synthetic substitutes where possible.

• Enforce least privilege for systems that can prompt models programmatically. Ensure service identities are separate and audited.

Supplier and contract management (ISO 27001 A.15)

• Treat AI vendors as critical suppliers: demand transparency on training data, prompt handling and security testing, and include contractual SLAs for safety and incident notification.

Monitoring, detection and response (ISO 27001 A.12, ISO 22301)

• Log prompts and model outputs (where retention and privacy rules permit) and feed them into SIEM/monitoring so anomalous prompting patterns can be detected.

• Design incident response playbooks that cover AI‑related incidents and integrate them with business continuity plans under ISO 22301.

People and culture (ISO 27001 A.7, A.10)

• Train staff on AI risks and prompt hygiene. Human operators must understand what to trust — and when to intervene. Synergos’ security awareness training can help embed this culture: https://synergosconsultancy.co.uk/usecure

• Run tabletop exercises that include AI failure scenarios so teams practise escalation and containment.

Certification and baseline hygiene

• Use frameworks such as ISO 27001 to document and formalise controls; consider Cyber Essentials for basic hygiene to reduce the attack surface: https://synergosconsultancy.co.uk/iasme‑certifications/

How to accept and manage residual risk — because “never” is not a plan

The NCSC’s warning means acceptance of some residual risk is realistic. That does not mean surrender. Instead, follow a layered approach: reduce likelihood through engineering controls, reduce impact through human review and segregation of duties, and shorten detection and recovery times with monitoring and rehearsed incident plans. Link those activities into your ISMS and BCMS so that senior management can make informed risk decisions rather than crossing their fingers and hoping the model behaves.

If you need a place to start, align an AI risk treatment plan to ISO 27001 control objectives and document it as part of your Statement of Applicability. For business continuity, ensure critical processes that depend on AI have manual fallbacks or alternate workflows captured in your ISO 22301 plans: https://synergosconsultancy.co.uk/iso‑22301‑business‑continuity‑management‑system‑bcms/

Next steps — pragmatic checklist for boards and security teams

• Identify AI assets and owners, add prompt‑injection to your risk register, and brief the board.

• Run adversarial prompt tests and integrate findings into development sprints.

• Implement output controls (human‑in‑the‑loop) for high‑impact actions and ensure logs feed detection systems.

• Review vendor contracts for security and incident notification clauses, and demand evidence of security testing.

• Train staff and run exercise playbooks that include AI failure and compromise scenarios.

Yes, prompt injection may be stubborn, but stubbornness is a quality best left to croissants, not cyber threats. Treat generative AI like the powerful but temperamental tool it is: respect it, control it, and prepare for when it tries to be cleverer than you.

If you want help aligning your AI risk management to ISO 27001 or shoring up continuity plans under ISO 22301, take a systematic approach and look for partners who can translate standards into practical controls — Synergos can help with certification pathways and training where needed: https://synergosconsultancy.co.uk/iso27001/

Organisations that act now — embedding AI‑specific threat modelling into their ISMS and rehearsing continuity plans — will be the ones that keep operating when the rest are surprised by a very modern kind of misbehaviour.