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The New Attack Surface: How AI Assistants Could Be Used to Social-Engineer Crypto Users

bbit coin

2026-02-06

10 min read

AI assistants like Gemini-powered Siri create new social-engineering risks for crypto users—learn realistic fraud scenarios and precise defenses.

Investors, traders and tax filers: the same AI that promises to make your phone a smarter financial assistant can also be weaponized to withdraw funds, export private keys, or authorize fraudulent transactions. In 2026 the integration of large-model assistants (notably the Gemini-powered Siri rollout across Apple devices) expanded a new attack surface: conversational AI with device privileges. If you treat voice assistants as consultants for your portfolio, you must also treat them as a risk vector.

Executive summary — what to know right now

Attackers now combine AI abuse, voice spoofing and social engineering to target crypto users. Practical defenses include limiting assistant privileges, enforcing hardware signing, upgrading wallet UX to require explicit human factors for authorization, and training high-risk users to spot AI-driven fraud flows. Below are scenarios, technical mitigations, user steps and policy recommendations you can apply today.

Why 2025–2026 changed the risk model

Two trends that accelerated the problem:

Platform AI consolidation: Apple’s 2025–26 integration of Gemini into Siri increased assistant capability and device-level access, enabling context-aware actions (calendar, contacts, wallets) that attackers can aim to abuse.
Rapid AI-enabled social engineering campaigns: In late 2025 and early 2026, widespread campaigns (including policy-violation style account-takeover waves on social platforms) showed how generative AI enables scalable, personalized attack content and voice cloning, increasing success rates against high-value targets.

High-probability fraud scenarios to watch

Below are realistic, prioritized scenarios an attacker could execute with advanced AI assistants and how they play out.

1) Voice-command transaction authorization via compromised assistant

Scenario: An attacker uses a cloned voice or a stolen device session to tell the assistant, “Send 1.5 BTC from my default wallet to this address — confirm.” The assistant’s integration with wallet apps or payment APIs could permit that if transaction authorization is insufficiently gated.

2) Prompt injection / context poisoning to extract credentials

Scenario: An attacker crafts conversational history or poisoned prompts (through email, calendar invites, or a malicious URL) that lead the assistant to read or reveal sensitive data, e.g., a BIP39 seed phrase cached for “quick recovery” or stored as a note.

Why it works: Assistants with broad cross-app search and summarization privileges can surface user secrets if apps do not mark them private or use secure enclave protections. Learn about new tools for detection and transparency in explainability projects like live explainability APIs.

Scenario: Over days, an AI builds rapport through the assistant — scheduling tax reminders, summarizing portfolio moves, then asks for an authorization code it claims is needed to “finalize tax-optimized transfer.” The user, trusting the assistant, complies.

Why it works: Humans are more likely to comply when an assistant appears helpful, personalized and context-aware. AI scales empathy and persuasion.

4) Enterprise/PA attack on executive devices

Scenario: An attacker compromises an executive’s calendar and has the assistant call their assistant to confirm a “payment approval.” The assistant can act as a trusted channel to route authorizations without direct human verification.

Why it works: Attackers weaponize trusted workflows; assistants blur lines between human and automated approvals.

Technical vectors: how attackers can reach assistant capabilities

Voice cloning & replay: High-fidelity voice synthesis to impersonate the owner, combined with remote command delivery (over a call or an IoT speaker).
Compromised device session: Stolen authentication tokens, SIM swaps, or compromised iCloud accounts grant control of assistant sessions.
Prompt injection: Malicious content in calendar invites, email, or web pages that the assistant ingests and acts upon.
Compromised third-party app: A malicious wallet or a compromised payment provider grants privileged API calls when routed by the assistant.
Social chain attacks: Attackers manipulate people in the victim’s social graph (assistants, PAs, bankers) to authorize actions indirectly.

Real-world example (composite case study)

In Q4 2025, a series of attacks targeted crypto tax filers: attackers sent realistic calendar invites labeled "Tax review: urgent" with an attached “reconciliation summary.” The AI assistant summarized the attachment and asked for clarification; the malicious content included a prompt asking the assistant to escalate, which triggered an authorization flow in a poorly designed wallet app. Funds were requested by an address that mimicked a tax-exempt entity. This composite illustrates the chain: content poisoning → assistant summarization → cross-app action → insufficient on-device signing.

Defense-in-depth: Immediate user & operational controls

Implement these steps today — ranked by effort and impact.

1) Lock down assistant privileges (High impact, low friction)

Disable assistant access to wallet apps and secure notes. On iOS/Android, revoke cross-app permissions and on-device indexing for finance apps.
Turn off “allow voice to unlock” for payment and wallet confirmations; require biometric or passcode for high-value actions.
Set the assistant to require a wake-word + user PIN for sensitive commands (where supported).

2) Enforce hardware signing for transactions (Critical)

Require that every blockchain transaction be signed by a hardware wallet or a secure element (Secure Enclave, Titan M). Software-only approvals triggered by an assistant must be rejected by policy.

3) Multisig and staged approvals (High-value accounts)

Use multisignature wallets where one signature is required from an off-device hardware key or multisig co-signers. Design workflows so assistants can prepare transactions but cannot complete them without physical or remote co-signer confirmation.

4) Transaction whitelisting and whitelabel UI (Developer & user)

Whitelist destination addresses for recurring transfers and require manual verification when a destination changes.
Make the transaction preview clearly show destination, amount in fiat, and a nonce step that requires human confirmation not possible via voice alone.

5) Audit trails and notifications (Operational)

Every assistant-initiated finance action must generate tamper-evident logs, server-side event records, and immediate OOB (out-of-band) notifications (SMS to a different device, email, or secure push) that require confirmation.

Design wallets and integrations assuming assistants will attempt to perform or induce actions. Key principles:

Assume untrusted context: Treat assistant requests as untrusted; require explicit user authentication via hardware key for critical actions.
Attestation & biometric binding: Use device attestation (e.g., Apple DeviceCheck, Play Integrity) and require biometric unlocks tied to secure elements. Ensure attestations are time-limited and context-aware.
Explicit consent flows: Build transaction confirmation flows that cannot be completed by voice inputs alone—use hardware buttons, animations, or tactile responses.
Limit data exposure: Mark seed phrases, private keys, and sensitive metadata with data-class protections that prevent assistant access and indexing.
Rate-limit assistant-triggered flows: Apply throttling, anomaly detection, and require MFA for unusually high-value requests.

Detection signals and indicators of compromise

Watch for these signals that suggest AI-assisted social engineering is underway:

Unusual assistant-initiated app launches or permission prompts.
Unexpected calendar invite chains that include attachments or URLs.
Authorization requests out of business hours or from unknown IPs/devices.
Secondary confirmations (SMS, email) not received after assistant actions.
Voice prints or device sessions used from unfamiliar geolocations.

User training: what to teach high-risk individuals

Train executives, traders, and tax filers with a focused syllabus:

Never disclose seed phrases, private keys, or full passphrases on a call, voice message or to an assistant.
Treat any assistant-initiated financial prompt as a potential fraud — verify via an independent channel before acting.
Use hardware wallets for large balances and multisig for operational accounts.
Be suspicious of calendar invites, last-minute “urgent” assistant requests, and any prompts that ask to bypass security (e.g., “skip 2FA for speed”).
Report odd assistant behavior immediately and preserve logs for investigation.

Policy & vendor recommendations (what to push platforms to do)

We recommend platform vendors and custodial services implement:

Assistant permission taxonomy: Clear OS-level categories that separate data-retention and action capabilities; finance-level actions should default to off.
Hardware-only approvals for native payments: Apple, Google and wallet vendors should require secure element confirmation for any fund movement higher than a configurable risk threshold.
Provable prompts & attestation: Assistants must produce cryptographic attestation of their prompts and actions when interacting with financial apps.
Transparency logs: Platforms should provide users with readable logs of assistant activity, including the source context used for decisions.

Advanced strategies for institutional actors

Institutions managing client crypto should adopt stronger controls:

Segregate devices: Office assistants and device accounts must be separate from trading/device access.
Use hardware signing services that never expose keys to assistant contexts (HSMs, offline signers).
Policy: prohibit assistants from performing account reconciliation or approval tasks unless under secure, recorded sessions with privileged account controls.
Threat modeling: include AI-assisted social engineering in tabletop exercises and red-team scenarios. For enterprise playbooks and large-scale incident response reference, see enterprise account-takeover playbooks.

Predictions: how the threat will evolve in 2026–2028

Expect the following trends that will affect wallet protection and AI safety:

Voice & context cloning improves: By late 2026 attackers will obtain near-perfect voice clones from public audio, increasing replay and impersonation risk.
Assistant-level integrations with financial rails: As assistants become payment endpoints (Apple Pay, Google Pay bridging to custodial wallets), attackers will focus on abusing authorization UX gaps.
Regulatory action: Regulators will require stronger attestation and auditability for assistant-initiated financial actions; expect new guidance in EU and US by 2027. For broader market and API predictions, see future data fabric and API predictions.
Defensive AI: AI will also be used to detect and block malicious prompts and to verify social-engineering attempts in real time.

Key takeaway: Treat AI assistants as a new front in your security posture — not an infallible helper. Assume they can be tricked, and design systems and behavior to make successful exploits very costly.

Practical checklist: protect your crypto now

Disable assistant access to finance apps and secure notes unless absolutely required.
Require hardware wallet signing for all transfers above a policy threshold.
Enable multisig for business accounts and large personal holdings.
Use out-of-band verification for any assistant-initiated financial action.
Revoke cached secrets and secure ephemeral tokens in apps; never store full seed phrases in notes or cloud backups.
Train staff and clients on AI-specific social engineering scenarios and run periodic drills.
Monitor for unusual assistant activity and enable detailed auditing where available.

What to do if you suspect AI-assisted fraud

Immediately freeze linked accounts where possible and revoke device session tokens.
Contact your custodian or exchange and supply logs showing assistant-initiated flows.
Preserve all relevant evidence (voice clips, calendar invites, assistant transcripts) and report to platform vendors and law enforcement.
Rotate keys and move remaining balances to hardware wallets with multisig while investigating.

Final thoughts — security-first adoption

The arrival of Gemini-powered Siri and similar assistants is a major usability win, but it also creates a powerful new attack surface. For finance-focused users — traders, investors and tax filers — the cost of complacency is high. A layered approach combining platform controls, secure developer practices, hardware-enforced signing and user training will blunt the risk of AI-driven social engineering.

Call to action

Start today: review assistant permissions on all devices, move large balances to hardware wallets, and run a simulated AI social-engineering drill for your team. Need a tailored risk assessment for your trading desk or tax workflow? Contact us for a security review and a hardening plan designed for 2026’s AI-enabled threat landscape.

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