A deceptively small UX convenience — allowing Copilot to accept a prefilled prompt from a URL — has been chained into a practical, one‑click data‑exfiltration technique that security researchers call Reprompt, while at the same time enterprise telemetry shows ChatGPT accounts for the lion’s share of generative‑AI data exposures, Microsoft is quietly adding file‑import convenience functions to Excel, and Alibaba has pushed its Qwen app further toward in‑chat commerce — a quartet of developments that together illustrate the productivity/security tension at the heart of today’s agentic AI rollout.
The convergence of conversational assistants, deep link conveniences and web‑hosted agent orchestration is creating new threat rails that traditional endpoint tooling struggles to observe. On one hand, researchers at Varonis Threat Labs publicly documented a proof‑of‑concept — dubbed Reprompt — showing how a maliciously crafted Copilot deep link can prefill a prompt (via a q parameter), coerce repeated actions, and accept server‑driven follow‑ups that together exfiltrate profile details, file summaries and chat memory from an authenticated Copilot Personal session after a single click. Concurrently, independent telemetry analyzed by third‑party security vendors indicates that a small number of consumer GenAI applications cause the majority of measured data exposures inside enterprise environments, with ChatGPT identified as the dominant vector in one large dataset. Those two stories — a low‑friction remote prompt injection and concentrated user behavior toward public chatbots — illustrate both a technique and a practical attack surface that attackers can exploit at scale. At the same time, productivity vendors continue to add conveniences: Microsoft has introduced new Excel import functions designed to make it trivial to load text and CSV data into spreadsheets, and Alibaba has upgraded its consumer Qwen AI app with in‑chat shopping, payments and travel booking — features that move assistants from “answer engines” toward transaction capable agents. Those feature moves increase value but also expand the places where sensitive data or authorizations might be exposed.
The Reprompt disclosure and the Harmonic telemetry report are a reminder that the hardest problems in modern AI security are not always exotic memory bugs or cryptographic failures; they are the interaction effects between convenience features, human behavior and distributed vendor infrastructure. Vendors must harden trust boundaries and persist enforcement across conversational lifecycles, while enterprises must couple rapid enablement with strict governance, semantic DLP and careful change control. The new Excel import functions and Alibaba’s move to agentic commerce illustrate why this balance matters: the same features that deliver measurable productivity — one‑click prompts, formula imports, in‑chat purchases — will drive more value if they are shipped with the kind of authorization, auditing and telemetry that make detectors and defenders effective.
Source: Computing UK https://www.computing.co.uk/news/20...k65r4Uqdy_M-JFesYNut7DRreLrfnNhwTi39lNdCAQ==]
Background / Overview
The convergence of conversational assistants, deep link conveniences and web‑hosted agent orchestration is creating new threat rails that traditional endpoint tooling struggles to observe. On one hand, researchers at Varonis Threat Labs publicly documented a proof‑of‑concept — dubbed Reprompt — showing how a maliciously crafted Copilot deep link can prefill a prompt (via a q parameter), coerce repeated actions, and accept server‑driven follow‑ups that together exfiltrate profile details, file summaries and chat memory from an authenticated Copilot Personal session after a single click. Concurrently, independent telemetry analyzed by third‑party security vendors indicates that a small number of consumer GenAI applications cause the majority of measured data exposures inside enterprise environments, with ChatGPT identified as the dominant vector in one large dataset. Those two stories — a low‑friction remote prompt injection and concentrated user behavior toward public chatbots — illustrate both a technique and a practical attack surface that attackers can exploit at scale. At the same time, productivity vendors continue to add conveniences: Microsoft has introduced new Excel import functions designed to make it trivial to load text and CSV data into spreadsheets, and Alibaba has upgraded its consumer Qwen AI app with in‑chat shopping, payments and travel booking — features that move assistants from “answer engines” toward transaction capable agents. Those feature moves increase value but also expand the places where sensitive data or authorizations might be exposed. Reprompt: the one‑click Copilot exfiltration explained
What researchers demonstrated
Varonis published a technical write‑up and proof‑of‑concept showing how three relatively innocuous behaviors can be composed into a stealthy exfiltration pipeline:- Parameter‑to‑Prompt (P2P) injection: the q query parameter in a Copilot deep link prepopulates the assistant input, so a crafted URL can inject instructions that run inside the victim’s authenticated session.
- Double‑request (repetition) bypass: client‑side safety checks may apply primarily to the initial request; instructing the assistant to “do it twice” or “try again” can let the second invocation succeed where the first was blocked.
- Server‑driven chain requests: once the assistant executes the benign‑looking first action, the attacker’s remote server can reply with follow‑up instructions that probe for specific fields and exfiltrate results in tiny chunks.
Why the vector is potent
- Extremely low friction: distribution is simple — a single Microsoft‑hosted link in email or chat can carry the payload, increasing click probability due to perceived vendor trust.
- Privilege inheritance: Copilot runs with the calling user’s context and Graph access; anything a user can legitimately read may be summarizable by the assistant unless specifically blocked.
- Visibility gaps: follow‑on instructions and vendor‑hosted fetches can hide exfiltration activity from local network monitors and endpoint detection systems.
Vendor response and current status
Varonis disclosed the issue under coordinated disclosure and published its write‑up on January 14, 2026. Microsoft deployed mitigations for Copilot Personal during its mid‑January Patch Tuesday updates; independent reporting confirms the vendor hardened the affected flows and released controls to administrators to help restrict consumer Copilot on managed devices. Public reporting at the time of disclosure reported no confirmed mass in‑the‑wild exploitation, but researchers and vendors cautioned that absence of evidence is not evidence of absence — the technique’s simplicity and scalability mean it could be weaponized quickly if copied by attackers.Practical impact for Windows users and IT teams
Immediate actions (triage)
- Apply updates now — ensure January 2026 Patch Tuesday updates are installed for Windows, Edge and any Copilot clients; vendors published mitigations that close the publicly disclosed Reprompt flow.
- Restrict Copilot Personal on corporate assets — where governance is required, prefer tenant‑managed Microsoft 365 Copilot with Purview, DLP and admin controls; block or remove the consumer Copilot app from managed devices where necessary. Microsoft has added controls (including a RemoveMicrosoftCopilotApp policy in Insider builds) to help admins manage the footprint.
- Treat AI deep links as high‑risk — implement URL rewriting or email‑gateway inspection for unknown Copilot deep links and train users to validate unexpected links via a secondary channel.
Tactical detection guidance
- Monitor for unusual Copilot‑hosted outbound requests to nonstandard endpoints after deep‑link activity.
- Watch for chained, small outbound transfers that correlate with user Copilot sessions rather than direct user web actions.
- Apply semantic DLP to Copilot read/write flows in enterprise Copilot instances; rely on tenant telemetry where possible rather than only local egress logs.
Architectural fixes vendors must adopt
- Treat all external inputs (URL parameters, page text, embedded artifacts) as explicitly untrusted prompt material.
- Persist safety and redaction enforcement across the entire interaction lifecycle — not just for the first invocation.
- Provide enterprise‑grade, auditable controls that enforce least privilege for assistants and expose actionable telemetry to defenders.
The enterprise exposure picture: ChatGPT and the 'big six'
What the data shows
Large‑scale monitoring of generative‑AI prompts in 2025 (22.4 million prompts in one publicly reported dataset) indicates that a very small number of consumer GenAI tools account for most observed data exposure events. Harmonic Security’s analysis — as described in multiple industry reports — found that six applications made up roughly 92.6% of potential exposure, with ChatGPT responsible for about 71.2% of exposures despite representing a smaller share of prompts. The finding underlines that sanctioning and controlling a handful of tools can materially reduce an organisation’s AI data exposure surface.Why ChatGPT dominates
- Widespread personal and free account use leads to ungoverned uploads of sensitive text.
- Users habitually copy/paste snippets of code or business text into public chatbots for convenience.
- Many CASBs and inline controls can’t robustly distinguish corporate accounts from personal free accounts at scale.
Practical enterprise controls
- Focus governance on the “big six” identified by your telemetry; prioritised controls can reduce over 90% of measured exposure in some datasets.
- Enforce account type differentiation — ensure corporate traffic cannot be routed to public/free model endpoints and block public model access from managed networks where possible.
- Semantic DLP and prompt scanning — deploy sensitive‑data detectors that are tuned for unstructured artifacts common in prompts (code, legal text, M&A fragments).
- User education and enablement — outright blocking is often impractical; provide sanctioned, auditable alternatives that retain productivity benefits while preventing leakage.
Productivity updates: Excel's new import functions
What changed
Microsoft introduced new Excel functions to import text‑based files directly into the spreadsheet grid: IMPORTTEXT (flexible import for TXT, CSV, TSV) and IMPORTCSV (a simplified, CSV‑targeted helper with smart defaults). These functions allow users to specify delimiter, rows to skip/take, encoding and locale, and accept local file paths or URLs as sources. The capability is currently being rolled out to Microsoft 365 Insider Beta channel users and requires a minimum build in Excel for Windows. Microsoft documents the IMPORTTEXT function in its support pages.Why this matters
- Simplifies ad‑hoc data ingestion for analysts who previously used Power Query or manual parse steps to load CSV/TXT content.
- Increases speed for routine tasks: quick imports via a formula are easier to script, copy and share inside workbooks.
- Potential governance caveat: importing by URL or local path can reintroduce data from uncontrolled locations into spreadsheets, so teams must apply data origin and refresh controls if they use IMPORTTEXT against network or web endpoints.
Recommended guardrails
- Limit use of URL imports to validated endpoints; require authentication modes that tie to organization accounts where possible.
- Educate users on the difference between local file imports and web imports; treat web imports like any other data ingestion that should flow through ETL/approval processes.
- Ensure workbook refresh processes and macros are reviewed — an automated IMPORTCSV cell can become an automated ingestion channel if combined with a scheduled refresh.
Alibaba Qwen moves from chat to commerce — and the risks that follow
What Alibaba announced
Alibaba’s consumer Qwen app has been upgraded to allow in‑chat shopping, payments (via Alipay), and travel bookings — effectively enabling a single conversational session to carry a user from intent to purchase and fulfillment. The features are in public testing in China and reflect a broader industry push toward agentic AI that acts on behalf of users rather than merely responding to queries. Reuters and Barron’s coverage highlight the integration across Taobao, Fliggy and Alipay and the product’s rapid user growth.Why this transition matters
- Convenience is monetizable: in‑chat commerce reduces friction and enables new revenue flows for platform owners.
- Attack surface expands: actions that create payments or bookings introduce authorization and anti‑fraud requirements that are materially different from read‑only assistants.
- Fraud and abuse risks: agentic flows that update bookings, apply discounts or manipulate prices can be subverted by prompt injection or by abuse of weak session controls, as prior PoCs against agent platforms have shown.
Security and governance implications
- Stronger authorization flows are required for transactional actions: single‑click confirmations are insufficient when monetary transfers are possible.
- Audit trails and non‑repudiation: transactional assistants must generate auditable evidence of consent, showing who authorized a booking or payment and under which identity context.
- Anti‑fraud controls: real‑time fraud signals (device ID, geofencing, velocity checks) should be integrated into agent decisioning, not bolted on later.
Cross‑cutting analysis: strengths, risks and what success looks like
Notable strengths
- Real productivity gains: integrated assistants and import helpers reduce mundane tasks and accelerate business workflows.
- User empowerment: no‑code agents and simple import functions democratize automation and data manipulation.
- Commercial opportunity: agentic commerce opens new revenue models and richer user experiences.
Systemic risks
- Privilege escalation through UX: seemingly benign conveniences (prefilled prompts, formula imports, in‑chat transactions) can be composed into high‑impact attacks.
- Visibility gaps: vendor‑hosted orchestration and chained server interactions reduce the signal visible to local defenses.
- Governance lag: enterprise controls, DLP policies and audit tooling often trail feature rollouts; unmanaged personal accounts accentuate the problem.
What “doing it right” requires
- Security‑first feature design — treat external inputs as untrusted, persist enforcement across interactions and refuse to rely on a one‑shot redaction model.
- Converged telemetry — correlate assistant actions, network flows and tenant logs to detect chained exfiltration patterns rather than isolated events.
- Granular governance — sandbox or disable consumer assistant features on corporate devices, enforce organizational authentication for imports and transactional actions, and adopt semantic DLP that understands the typical shapes of prompts (code snippets, contracts, financial data).
- User enablement — provide sanctioned, auditable alternatives so users do not route sensitive work through public, unsanctioned chat accounts.
Flagging unverifiable and caveated claims
- The public Varonis PoC demonstrates feasibility in lab conditions and the vendor has issued mitigations; however, there is no public evidence at disclosure time of mass in‑the‑wild exploitation. Absence of observed exploitation should be treated cautiously — the Reprompt pattern is low friction and easily reproducible.
- Harmonic Security’s dataset covers 22.4 million prompts and the reported exposure percentages come from that dataset; different organizations or geographies may see materially different distributions. Treat the “big six” prioritization as a practical heuristic to reduce exposure quickly, not as a universal truth that absolves organizations from addressing long‑tail risks.
Recommended action checklist for IT leaders (prioritised)
- Patch and verify: Confirm January 2026 Copilot and Windows updates are installed and verify client builds against vendor release notes.
- Inventory and block: Identify unmanaged GenAI endpoints used across your estate; block or control access to public models from managed devices where necessary.
- Harden assistant invocation: Disable deep‑link prefill execution where possible or force re‑authentication/consent before any assistant can act on privileged data.
- Semantic DLP: Deploy and tune detectors for code, legal text, M&A and financial artifacts in prompts and uploads.
- Govern imports and transactions: Treat IMPORTTEXT/IMPORTCSV and in‑chat commerce as data ingress/egress channels; require policy‑approved endpoints and stronger approval flows for transactions.
- User enablement: Offer sanctioned tools — tenant‑managed Copilot, internal agent platforms or vetted enterprise LLMs — with clear, audited workflows.
The Reprompt disclosure and the Harmonic telemetry report are a reminder that the hardest problems in modern AI security are not always exotic memory bugs or cryptographic failures; they are the interaction effects between convenience features, human behavior and distributed vendor infrastructure. Vendors must harden trust boundaries and persist enforcement across conversational lifecycles, while enterprises must couple rapid enablement with strict governance, semantic DLP and careful change control. The new Excel import functions and Alibaba’s move to agentic commerce illustrate why this balance matters: the same features that deliver measurable productivity — one‑click prompts, formula imports, in‑chat purchases — will drive more value if they are shipped with the kind of authorization, auditing and telemetry that make detectors and defenders effective.
Source: Computing UK https://www.computing.co.uk/news/20...k65r4Uqdy_M-JFesYNut7DRreLrfnNhwTi39lNdCAQ==]
