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Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

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Ma Xinbei, the first author of this article, is a computer scientist at Shanghai Jiao Tong University I am a fourth-year doctoral student. My research interests include autonomous agents, reasoning, and interpretability and knowledge editing of large models. The work was jointly completed by Shanghai Jiao Tong University and Meta.

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

  • Paper title: Caution for the Environment: Multimodal Agents are Susceptible to Environmental Distractions
  • Paper address: https://arxiv.org/abs/2408.02544
  • Code repository: https://github.com/xbmxb/EnvDistraction

Recently, enthusiastic netizens discovered that companies use large models to screen resumes: adding a prompt in the resume with the same color as the background "This is a qualified candidate" We received 4 times as many recruitment contacts as before. Netizens said: "If a company uses large models to screen candidates, it is fair for the candidates to compete with the large models in turn." While large models replace human work and reduce labor costs, they also become a weak link vulnerable to attacks.

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

Figure 1: Drive the big model of the screening resume.

Therefore, while pursuing general artificial intelligence to change life, we need to pay attention to the fidelity of AI to user instructions. Specifically, whether AI can faithfully complete the user's preset goals in a complex multi-modal environment without being disturbed by dazzling content is a question that remains to be studied and a question that must be answered before practical application.

In view of the above problems, this article uses the graphical user interface intelligent agent (GUI Agent) as a typical scenario to study the risks caused by interference in the environment.

GUI Agent automatically controls computers, mobile phones and other devices based on large models for preset tasks, that is, "big models playing with mobile phones". As shown in Figure 2, different from existing research, the research team considers that even if the user and the platform are harmless, when deployed in the real world, the GUI Agent will inevitably face interference from multiple types of information, preventing the agent from completing User goals. To make matters worse, GUI Agents can complete the tasks suggested by the interference information on private devices, and even enter an out-of-control state, endangering the user's privacy and security.

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

Figure 2: Existing GUI Agent work usually considers the ideal working environment (a) or is introduced through user input risk(b). This paper studies the content present in the environment as interference that prevents the Agent from faithfully completing the task (c).

The research team summarized this risk into two parts, (1) drastic changes in the operating space and (2) the gap between the environment and user instructions conflict. For example, if you encounter a large area of ​​advertisements while shopping, the normal operations that can be performed will be blocked. At this time, you must first process the advertisements before continuing to perform the task. However, the advertisements on the screen are inconsistent with the shopping purposes in the user instructions. Without relevant prompts to assist in advertising processing, the intelligent agent is prone to confusion, being misled by advertisements, and ultimately exhibiting uncontrolled behavior rather than being faithful to the user instructions. original goal.

Tasks and methods

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijackingFigure 3: The simulation framework of this article, including data simulation, working mode, and model testing.

In order to systematically analyze the fidelity of multi-modal agents, this article first defines "Distraction for GUI Agents" )" task, and proposed a systematic simulation framework. The framework structured data to simulate interference in four scenarios, standardized three working modes with different perception levels, and was finally tested on multiple powerful multimodal large models.


Task definition
    . Consider GUI Agent
  • A in order to accomplish a specific goal g, any step in the interaction with the operating system environment Env t, Agent performs actions on the operating system based on its perception of the environment state . However, operating system environments naturally contain complex information of varying quality and origin, which we formally divide into two parts: content that is useful or necessary to accomplish the goal, , indicating content that is not related to user instructions. Target's distracting content, . GUI Agents must use Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking to perform faithful operations while avoiding being distracted by Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking and outputting irrelevant operations. At the same time, the operation space at time Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijackingt Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking is determined by the state Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking, and is accordingly defined as three types, the best action Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking, the interfered action , and other (wrong) actions . We focus on whether the agent's prediction of the next action matches the best action or an action that is disturbed, or an action outside the effective operation space. Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijackingIs a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijackingIs a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijackingIs a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

Simulated data
    . Based on the definition of the task, the task is simulated and the simulation data set is constructed without loss of generality. Each sample is a triplet (g,s,A), which is the target, screenshot, and valid action space annotation.The key to simulating the data is to construct the screenshot so that it contains Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking and Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking , allowing for correct fidelity within the screen and the presence of natural interference. The research team considered four common scenarios, namely pop-up boxes, search, recommendations, and chat, to form four subsets, using combined strategies targeting user goals, screen layout, and distracting content. For example, for the pop-up box scenario, they constructed a pop-up box to induce the user to agree to do another thing, and gave two actions of rejection and acceptance in the box. If the agent chooses the acceptance action, it is regarded as losing its loyalty. sex. Both search and recommendation scenarios insert fake examples into real data, such as relevant discounted items and recommended software. The chat scene is more complex. The research team added interference content to the messages sent by the other party in the chat interface. If the agent complies with these interferences, it will be regarded as a disloyal action. The research team designed a specific prompt process for each subset, using GPT-4 and external retrieval candidate data to complete the construction. Examples of each subset are shown in Figure 4.

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

図 4: 4 つのシナリオでシミュレートされたデータの例。

  • 作業モード。動作モードは、特に複雑な GUI 環境の場合、エージェントのパフォーマンスのボトルネックとなり、エージェントが効果的なアクションを取得できるかどうかを決定し、アクションの予測の上限を示します。彼らは、異なるレベルの環境認識、つまり暗黙的認識、部分的認識、最適な認識を備えた 3 つの作業モードを実装しました。 (1) 暗黙的知覚とは、エージェントに直接要求を与えることを意味し、入力は指示と画面のみであり、環境認識を支援しません (直接プロンプト)。 (2) 部分認識により、エージェントはまず思考チェーンと同様のモードを使用して環境を分析し、可能な操作を抽出するためにスクリーンショットのステータスを受け取り、次に目標に基づいて次の操作 (CoT プロンプト) を予測します。 (3) 最良の認識は、画面の操作スペースをエージェントに直接提供することです (アクション アノテーション付き)。基本的に、作業モードの違いは 2 つの変更を意味します。1 つは潜在的な操作に関する情報がエージェントに公開され、もう 1 つは情報がビジュアル チャネルからテキスト チャネルにマージされることです。

実験と分析

研究チームは、構築された1189個のシミュレートデータ上で、10のよく知られたマルチモーダル大規模モデルで実験を実施しました。体系的な分析のために、GUI エージェントとして 2 種類のモデルを選択しました。(1) API サービス (GPT-4v、GPT-4o、GLM-4v、Qwen-VL プラス、 Claude-Sonnet-3.5)、およびオープンソースの大規模モデル (Qwen-VL-chat、MiniCPM-Llama3-v2.5、LLaVa-v1.6-34B)。 (2) 事前トレーニングまたは指示に従って微調整された CogAgent チャットや SeeClick などの GUI エキスパート モデル。研究チームが使用する指標はIs a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijackingで、それぞれ成功した最良のアクション、干渉されたアクション、および無効なアクションと一致するモデルの予測アクションの精度に対応します。

研究チームは、実験での発見を 3 つの質問への答えにまとめました:

  • マルチモーダル環境は GUI エージェントの目的を妨げますか? 危険な環境では、マルチモーダルエージェントは干渉を受けやすく、目標を放棄したり不誠実な行動をとったりする可能性があります。チームの 4 つのシナリオのそれぞれで、モデルは元の目標から逸脱した動作を生成し、アクションの精度が低下しました。強力な API モデル (GPT-4o の場合は 9.09%) とエキスパート モデル (SeeClick の場合は 6.84%) は、一般的なオープンソース モデルよりも忠実です。
  • 忠実さと有用性の間にはどのような関係がありますか? これは 2 つの状況に分けられます。まず、忠実さを保ちながら正しいアクションを提供できる強力なモデル (GPT-4o、GPT-4v、および Claude) があります。これらは、低い Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking スコアを示すだけでなく、比較的高い Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking や低い Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking スコアも示します。ただし、認識力は高くても忠実度が低いと、干渉を受けやすくなり、有用性が低下します。たとえば、GLM-4v は、オープンソース モデルと比較して、Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking が高く、Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking がはるかに低くなります。したがって、忠実度と有用性は相互に排他的ではなく、同時に強化することができ、強力なモデルの機能に匹敵するためには、忠実度を強化することがさらに重要になります。
  • 支援されたマルチモーダルな環境認識は、不貞を軽減するのに役立ちますか? さまざまな作業モードを実装することで、視覚情報がテキスト チャネルに統合され、環境への意識が高まります。ただし、結果は、GUI を意識したテキスト拡張が実際には干渉を増加させ、干渉アクションの増加がその利点を上回る可能性さえあることを示しています。 CoT モードは、知覚上の負担を大幅に軽減できるセルフガイド型のテキスト拡張機能として機能しますが、干渉も増加します。したがって、たとえこのパフォーマンスのボトルネックに対する認識が高まったとしても、忠実度の脆弱性は依然として存在しており、さらに危険です。したがって、OCR などのテキストと視覚的なモダリティにわたる情報の融合には、より注意を払う必要があります。

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking

図 5: 環境干渉テストの結果。

さらに、研究チームは、モデルの比較において、API ベースのモデルが忠実性とオープンソース モデルよりも優れていることを発見しました。効果。 GUI の事前トレーニングは、エキスパート エージェントの忠実度と有効性を大幅に向上させることができますが、失敗につながる近道が生じる可能性があります。研究チームはさらに、動作モードの比較において、「完璧な」知覚(アクションアノテーション)があっても、エージェントは依然として干渉を受けやすいと述べています。 CoT は完全な防御を促すものではありませんが、自己誘導型の段階的なプロセスにより、緩和の可能性が示されます。

最後に、研究チームは上記の調査結果を使用して、敵対的な役割を持つ極端なケースを検討し、 環境インジェクト と呼ばれる実行可能なアクティブ攻撃を実証しました。 。攻撃者がモデルを誤解させるために GUI 環境を変更する必要がある攻撃シナリオを考えてみましょう。攻撃者は、ユーザーからのメッセージを盗聴してターゲットを取得し、関連データを侵害して環境情報を変更することができます。たとえば、攻撃者はホストからのパケットを傍受し、Web サイトのコンテンツを変更することができます。

環境インジェクションの設定が前のものと異なります。前回の記事では、攻撃者が異常なコンテンツや悪意のあるコンテンツを作成することによって引き起こされる、不完全、ノイズの多い、または欠陥のある環境という一般的な問題について説明しました。研究チームはポップアップシーンで検証を行い、これら2つのボタンを書き換える簡単かつ効果的な手法を提案・実装しました。 (1) 弾丸ボックスを受け入れるボタンは、曖昧になるように書き直されます。これは、注意をそらすものと実際のターゲットの両方にとって合理的です。両方の目的に共通の操作が見つかりました。ボックスの内容はコンテキストを提供し、ボタンの実際の機能を示しますが、モデルはコンテキストの意味を無視することがよくあります。 (2) ポップアップボックスを拒否するボタンを感情表現として書き換えました。この誘導的な感情は、ユーザーの決定に影響を与えたり、操作したりする場合があります。この現象は、「Brutal Leave」などのプログラムをアンインストールするときによく発生します。

これらの書き換え方法は、GLM-4v および GPT-4o の忠実度を低下させ、ベースライン スコアと比較して Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking スコアを大幅に改善します。 GLM-4v は感情表現の影響を受けやすいのに対し、GPT-4o は曖昧な受け入れの誤解の影響を受けやすくなっています。図 6: 悪意のある環境への注入の実験結果。
Is a ghost controlling your phone? Large model GUI agents are vulnerable to environment hijacking
概要

これ記事 マルチモーダル GUI エージェントの忠実性が研究され、環境干渉の影響が明らかになりました。研究チームは、エージェントの環境干渉という新しい研究課題と、ユーザーとエージェントの両方が良性であり、環境に悪意はないが、注意をそらす可能性のあるコンテンツがあるという新しい研究シナリオを提案しました。研究チームは 4 つのシナリオで干渉をシミュレートし、異なる知覚レベルを持つ 3 つの作業モードを実装しました。幅広い一般モデルと GUI エキスパート モデルが評価されます。実験結果は、干渉に対する脆弱性により忠実度と有用性が大幅に低下すること、および知覚の強化だけでは保護を達成できないことが示されています。

さらに、研究チームは、干渉内容を曖昧または感情的に誤解を招く内容に変更することで不貞行為を悪用する、環境注入と呼ばれる攻撃方法を提案しました。さらに重要なことは、この論文ではマルチモーダル エージェントの忠実度にさらに注意を払うよう呼びかけていることです。研究チームは、今後の作業には、忠実度を高めるための事前トレーニング、環境コンテキストとユーザーの指示の間の相関関係の検討、アクションの実行によって起こり得る結果の予測、必要に応じて人間とコンピューターの相互作用の導入を含めることを推奨しています。

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