


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

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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
Figure 3: The simulation framework of this article, including data simulation, working mode, and model testing.
- . 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 to perform faithful operations while avoiding being distracted by
and outputting irrelevant operations. At the same time, the operation space at time
t
is determined by the state
, and is accordingly defined as three types, the best action
, 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.
- . 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


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。

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 スコアを示すだけでなく、比較的高い
や低い
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、
が高く、
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。


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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