Your AI reads everything—so do Attackers.
Executive Summary (for busy leaders)
AI assistants now read your emails, tickets, and files to save time. Attackers are sneaking hidden instructions—poisoned AI prompts—into that same content so your AI will “help” them instead. The result: data leakage, privilege escalation, and automated changes you never approved.
What to do now: add AI-aware email defences, isolate assistants with least-privilege access, harden your automation chains with approvals and rate limits, and keep humans in the loop 24/7. Major players now treat indirect prompt injection as a first-order risk and recommend defence-in-depth because no single filter is reliable on its own.
Situation: AI is now both tool and target
AI drafts replies, summarises documents, and triggers workflows across Microsoft 365 and Google Workspace. That convenience creates a new attack path: if an adversary can plant content your assistant will later read, they can steer what it does. Security researchers and industry teams report active attacks against both AI assistants and the AI features in security tools. Email, ticketing, and doc platforms are increasingly in the blast radius because they feed assistants the very context attackers aim to corrupt.
We’re also seeing the web itself split into two worlds: one for humans, one for AI. Recent research shows “parallel poisoned web” pages that serve benign content to users but a different, malicious version to AI agents—content ordinary crawlers never see. That makes the attack exceptionally stealthy and tailored to the way assistants ingest data.
The bottom line: AI has expanded both the attack surface and the blast radius of everyday business workflows. It’s now routine to find hidden prompts that hijack assistants inside perfectly normal-looking emails and docs.
What “poisoned AI prompts” look like
A poisoned AI prompt is a malicious instruction embedded where your assistant will look: an email thread, a comment in a shared doc, a ticket update, a wiki footnote, even the alt text of an image. When the assistant ingests that content, it may follow hidden instructions—e.g., leak a spreadsheet, escalate a ticket, or trigger an automation.
Why this works: assistants are optimised to follow instructions and to treat surrounding context as trustworthy. RAG (retrieval-augmented generation) increases the risk because tampered context quietly steers outputs and actions without obvious cues to the human user. The OWASP GenAI project now lists LLM01:2025 Prompt Injection at the top of its risks, emphasising that injections can be invisible to humans yet still parsed by the model.
Modern prompt injection is not just “Ignore previous instructions.” It includes obfuscated payloads (e.g., base64/ASCII smuggling), adversarial formatting, and cross-document “breadcrumb” prompts designed to activate only when specific plug-ins or tools are available. Security teams have publicly demonstrated end-to-end exfiltration by lacing third-party content with instructions that Copilot-style assistants execute when summarising or drafting replies.
Why email is the perfect delivery vehicle
Email remains the most universal business channel—and it’s now an AI-augmented workspace. Assistants scan inboxes and file stores to prepare summaries, propose replies, and kick off automations. That makes email a high-throughput, high-trust ingestion path straight into your assistant’s “eyes and ears.”
Generative AI lets attackers mass-produce polished, on-brand emails that evade old-school filters. Researchers have documented malicious prompts hidden in benign-looking emails that pass basic authentication checks (SPF/DKIM) and only become dangerous when the assistant reads them. The poisoned content may sit dormant for days until a user asks their assistant for a digest, a triage pass, or a “clean up my inbox” task—and then the hidden instructions fire.
It gets worse when HTML and images are involved. Attackers can embed instructions in obscure parts of the message body, hide them off-screen with CSS, or pack them into alt text, where they’re seen by screen readers and AI parsers but not by a casual human glance. McKinsey and others note that both attackers and defenders are using AI at scale; the volume and quality of malicious content is rising, and email is the cheapest distribution channel.
When defenders’ own AI becomes an attack surface
Modern security stacks include AI features like auto-reply to tickets, smart routing, “suggested actions,” and automated playbooks across SOAR/XDR/ITSM. If a malicious instruction slips into that flow, your tools can be tricked into revealing sensitive details, changing settings, or creating escalation paths—turning defence automation into an unguarded back door.
Microsoft’s security response centre explicitly warns that indirect prompt injection is prevalent and recommends layered mitigations spanning content filtering, allow-lists for tool use, provenance checks, and human approvals before sensitive actions. In other words: treat assistant-triggered actions as untrusted by default.
Independent research backs this up. Demonstrations have shown assistant-driven exfiltration when analysing untrusted content, including proof-of-concepts that pivot from an email or doc to sensitive data through tool or API invocation. This is classic “adversary-in-the-middle,” except the “middle” is your AI agent.
Identity abuse and the “confused deputy” problem
Assistants often act on behalf of users and hold broad permissions. Attackers exploit this with confused-deputy patterns: a low-privilege actor gets a high-privilege AI to perform restricted actions. In cloud suites, assistants commonly inherit scopes that exceed what a careful human would grant to an intern or contractor. If the assistant treats any ingested text as a credible instruction channel, those scopes are a liability.
We also see API spoofing scenarios where prompts nudge assistants to call legitimate APIs (Microsoft 365, Google Workspace) in ways the business didn’t intend—creating forwarding rules, sharing documents externally, or “fixing” permissions that were restrictive by design. Testing across Copilot-style systems has shown that third-party content can influence assistant behaviour without explicit user intent, leading to data exfiltration and integrity damage.
The risk picture for Canadian SMBs
For small and mid-sized businesses (5–250 employees; up to $50M CAD), the attraction of assistants is efficiency. The trade-off is silent, scalable risk:
- Silent data leaks. Summaries or auto-replies that include sensitive information or that forward content externally “to help.”
- Fraudulent changes. Ticket or workflow rules altered by poisoned content that your AI “helpfully” enacted.
- Privilege escalation. AI performs admin-level actions “on behalf” of a user with weaker permissions.
- Automation cascades. One tainted message propagates across digests, task lists, dashboards, and search—undermining trust in everything downstream.
- Compliance exposure. Under PIPEDA and Québec’s Law 25, unauthorised disclosure or cross-border sharing by an assistant can trigger notification and record-keeping obligations—regardless of whether a human clicked “send.”
NIST’s Generative AI Profile stresses that the number and power of attacks currently exceed the available mitigations, pushing organisations toward layered, measurable controls rather than silver bullets.
What good looks like: four control pillars
1) Make filters AI-aware
Legacy controls (SPF, DKIM, IP reputation) aren’t enough. You need detections tuned to LLM-style writing patterns, instruction cues, and anomalous behaviour:
- Spot unusually “clean,” stylometry-consistent messages at scale.
- Flag instruction-like text (e.g., “Ignore all previous…”, “Use this secret…”) and obfuscation (base64/ASCII blocks, steganographic HTML).
- Scan image alt-text and hidden HTML regions; quarantine or strip suspicious instructions in emails, notes, and tickets.
- Validate what your assistants “remember.” If you use vector notes or retrieval caches, set short time-to-live, track provenance, and purge anything that fails re-validation.
Researchers have documented hidden prompts in seemingly harmless emails; treat content as untrusted until proven clean.
2) Isolate assistants and enforce least privilege
Contain the blast radius:
- Run assistants in confined tenants/workspaces with read-only defaults.
- Scope tokens and app permissions to the minimum set; require step-up (MFA + approval) for sensitive actions like external sharing, deletion, or permission changes.
- Apply Zero-Trust checks to every instruction—routine or not.
- Maintain allow-lists for tool and API usage so an injected prompt can’t make the assistant call unexpected services.
This is Microsoft’s recommended posture against indirect prompt injection: layered, deterministic controls that gate actions independent of model “intent.”
3) Harden the automation chain
Prevent single-message catastrophes:
- Add approvals, guardrails, and rate limits to SOAR, ITSM, and XDR playbooks.
- Enforce context checks: “Was this request user-initiated?” “Is the source external or high-risk?” “Has a human reviewed the content?”
- Require dual-control for rule changes to email routing, inbox processing, and ticket escalations.
- Instrument everything—log assistant reads/writes, tool calls, and suppress auto-execute paths when risk signals are present.
Real-world threat write-ups show indirect prompt injection bypassing email security products when actions aren’t gated. Build scepticism into the workflow.
4) Keep people in the loop
Technical controls fail quietly if staff don’t recognize weird AI behavior. Train your team to spot unusually polished lures, sudden “urgent” tone changes, and assistants that propose out-of-character actions. Add a one-click “Report AI weirdness” path to SecOps so you can investigate before damage spreads. Independent guidance emphasizes human validation as tactics evolve faster than tooling.
A practical 30-day roadmap
Week 1 — Rapid risk check (CIO/CISO, IT Ops, SecOps)
- Inventory assistants, plug-ins, and automations; map each to its data scopes and identities.
- Turn on mailbox and doc-store logging for assistant access; route logs to SIEM.
- Disable auto-execute actions by default; require approvals on write/delete/external share.
- Create an Assistant Permission Matrix that lists: data each assistant may read; actions allowed; actions that always require human approval.
Week 2 — Email & context hygiene (SecOps, M365/Google admins)
- Enable advanced phishing protections and external-sender banners.
- Deploy AI-aware detections for instruction phrases and encoded payloads; scan alt-text and off-screen HTML.
- Start an expiry/validation policy for assistant memory (vectors/notes); enforce strict provenance tagging for retrieved docs.
- Quarantine or strip suspicious content before it hits end-users or assistants.
Week 3 — Least-privilege & Zero Trust (Identity team, app owners)
- Rotate tokens; reduce scopes; convert broad app permissions to resource-scoped, per-site access.
- Implement Zero-Trust Network Access (ZTNA) and Zero-Trust App Control (ZTAC) to sandbox assistants and data-movement tools.
- Add rate limits + dual approvals to SOAR/ITSM rules.
- Build allow-lists for tool invocation and deny risky functions (e.g., shell, external web fetch) unless explicitly needed.
Week 4 — Drill & validate (SecOps, IR, business owners)
- Run a tabletop: poisoned AI prompt in a vendor email leads to ticket escalation + data leak.
- Measure MTTR to isolate the assistant identity, revoke tokens, roll back rule changes, and purge tainted memory.
- Publish a concise AI Incident Containment SOP and embed it into your IR playbooks.
This is not theory. Multiple teams have published demonstrations of prompt-to-exfiltration paths via assistants and email. Plan and practise before an attacker forces the lesson.
Technical deep-dive: detection and prevention ideas you can use today
Content cues to flag
- Imperatives aimed at an AI: “Ignore previous instructions…,” “Summarise and forward…,” “Use this secret…,” “Evaluate this base64…”
- Long base64-like blocks (200+ characters) adjacent to verbs like decode, execute, render, eval.
- HTML/CSS that hides text off-screen (e.g.,
display:none, negative margins) or uses tiny fonts and colour-matched text. - Excessive alt text length, or alt text containing instruction phrases.
Gateway and SIEM logic (starter idea)
If (instruction phrase OR encoded payload) AND (external sender OR new thread) → quarantine, strip risky sections, and open a ticket for review. Correlate with assistant activity logs; if the same message was read by an assistant, raise severity and revoke any pending auto-actions.
Assistant memory hygiene
- Use short TTLs for vector notes and retrieved snippets.
- Tag each chunk with provenance (source, sender, time, risk score).
- Block retrieval from sources that fail DMARC or originate from new/untrusted domains.
- Build a “safe context” allow-list (company wiki, vetted SharePoint, approved vendors) and require human approval before assistants learn from anything else.
Tool and API use
• Require explicit allow-listing for tool calls (e.g., “send email,” “share file,” “change permission”).
• Prompt injection can be mitigated with policy outside the model: if the model suggests a sensitive action based on external content, the action must be gated by an approval flow with MFA and logging. Microsoft’s guidance favours exactly this deterministic gatekeeping.
Governance, KPIs, and ownership
Controls are only as good as their upkeep. Assign clear owners and track measurable outcomes:
- Identity: % of assistant identities with read-only scopes; # of tokens rotated in last 90 days.
- Email hygiene: # of instruction-like detections per 1,000 emails; % quarantined with analyst review.
- Automation safety: % of workflows with dual control; # of auto-execute paths remaining (target: 0).
- Response readiness: MTTR to isolate an assistant; # of table-tops completed quarterly.
- Compliance: documented decision trail for assistant-triggered actions; audit logs retained per policy.
NIST’s GenAI profile encourages this shift from ad-hoc controls to risk-based, observable safeguards aligned to business outcomes.
How Fusion Cyber protects you from AI-enabled threats
We design controls assuming attackers will target both your users and your AI.
- 24/7/365 Monitoring & Threat Containment. Our analysts watch for anomalous assistant behaviour and can isolate endpoints, identities, or cloud accounts the moment we detect suspicious activity.
- Advanced Email Protection & Anti-Phishing. AI-aware analysis to flag LLM-crafted lures and detect hidden prompt content in bodies, HTML, and alt text.
- Advanced SaaS Defence. Least-privilege policies and behavioural monitoring across Microsoft 365/Google Workspace to block confused-deputy and API-spoofing attempts.
- Zero-Trust Network & Application Control. Sandbox assistants and automations within tight guardrails so a single poisoned message can’t cause outsized harm.
- Proactive Threat Hunting. We hunt poisoned context across mailboxes, SharePoint/Drive, and ticketing systems; we purge tainted assistant memory and verify provenance.
- Vulnerability & Attack-Surface Management. We close misconfigurations that let automations over-reach (e.g., overly broad app consents, legacy forwarding rules).
- Security Awareness & Phishing Simulation. We teach teams to recognise AI-polished lures and to report odd assistant actions quickly.
- Backups for Microsoft 365 & Google Workspace. We ensure fast recovery if poisoned automations alter or delete data.
And we stand behind it. Our cybersecurity guarantee covers the cost of threat containment, incident response, remediation, eradication, and business recovery if a company-wide breach occurs despite protections (coverage limits apply).
Final Thoughts
AI is now both a tool and a target. Prompt poisoning turns ordinary content into a control channel for attackers. Protecting your business requires AI-aware email security, zero-trust guardrails, tight identity controls for assistants and automations, and human-led 24/7 monitoring ready to intervene.
This threat isn’t hypothetical. From vendor threat spotlights documenting hidden instructions in emails, to live demonstrations of assistant-driven exfiltration, to new techniques that serve poisoned web pages only to AI agents, the evidence is mounting—and current best practice is layered, sceptical, and measurable.
Featured links:
Microsoft on Indirect Injection
FAQ:
Are RAG systems inherently less safe?
Not inherently—but risk rises when retrieval pulls from untrusted or tampered sources. Mitigate with provenance checks, allow-listed repositories, short memory TTLs, and human approval before assistants act on externally sourced context.
What permissions should AI assistants have by default?
Default to read-only. Gate sensitive actions—external sharing, deletion, permission changes—behind MFA and human approval. Use scoped tokens, deny risky tools by default, and log every assistant read/write and tool call for auditability.
What’s the fastest starting move for SMBs?
Disable auto-execute actions, enable advanced phishing banners, scan alt-text/hidden HTML for instruction patterns, and publish a one-page “AI incident containment” SOP. In parallel, map each assistant’s data scope and reduce token privileges.
PROBLEM
Attackers hide instructions in emails, docs, and tickets that AI assistants will obey.
IMPACT
Data leaks, rule changes, and privilege escalation—often without human awareness.
SOLUTION
AI-aware email inspection, least-privilege assistants, gated automations, provenance-checked RAG, and 24/7 SOC oversight.
CONSEQUENCE
One poisoned message can cascade across workflows, corrupt decisions, and trigger compliance issues—costing far more than implementing layered controls today.
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That’s why we offer a cybersecurity guarantee: in the very unlikely event that a breach gets through our multi-layered, 24/7 monitored defenses, we will handle all:
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