Incident Types: The types of cybersecurity incidents that have occurred include Cyber Attack, Vulnerability, Data Leak and Breach.

Detection and Response: The company detects and responds to cybersecurity incidents through an communication strategy with company notified affected users, and remediation measures with update to address the issue, and remediation measures with implemented mitigations to address the specific attack demonstrated by the researchers, and third party assistance with adversa ai (research/disclosure), and remediation measures with audit ai routing logs for suspicious activity, remediation measures with implement cryptographic routing (non-user-input parsing), remediation measures with add universal safety filters across all model variants, and communication strategy with public disclosure via research report, communication strategy with media outreach (e.g., google news, linkedin, x), and enhanced monitoring with monitor for trigger phrases (e.g., 'respond quickly', 'compatibility mode'), and and third party assistance with radware (disclosure), third party assistance with bugcrowd (reporting platform), and containment measures with vulnerability patching, containment measures with safety guardrail enhancements, and remediation measures with prompt injection defenses, remediation measures with autonomous agent behavior restrictions, and communication strategy with public disclosure via recorded future news; emphasis on bug bounty program, and enhanced monitoring with likely (implied by 'continual safeguard improvements'), and and third party assistance with radware (discovery and analysis), and containment measures with openai patch for deep research tool (august 2025), containment measures with disabling vulnerable integrations (recommended), and remediation measures with input sanitization for hidden prompts, remediation measures with restricting ai agent access to third-party apps, and communication strategy with public disclosure by openai and radware, communication strategy with media coverage (fox news, cyberguy.com), and enhanced monitoring with recommended for ai agent activities, and and containment measures with model training to ignore malicious instructions, containment measures with overlapping guardrails, containment measures with detection/blocking systems, and remediation measures with red-teaming exercises, remediation measures with security controls for logged-in/logged-out modes, remediation measures with ongoing research into mitigation strategies, and communication strategy with public acknowledgment by openai ciso (dane stuckey), communication strategy with x post detailing risks and mitigations, communication strategy with media statements to the register, and and incident response plan activated with yes (openai notified and working on fixes), and third party assistance with tenable research (vulnerability disclosure), and containment measures with patching vulnerabilities (ongoing), containment measures with enhancing prompt injection defenses, and communication strategy with public disclosure via tenable research report, communication strategy with media statements (e.g., hackread.com), and enhanced monitoring with likely (for prompt injection attempts)..

Common Attack Types: The most common types of attacks the company has faced is Vulnerability.

Identification of Attack Vectors: The company identifies the attack vectors used in incidents through pictureproxy.php component, Malicious document uploaded to ChatGPT or shared to Google Drive, User Prompt Input FieldAI Routing Layer, Malicious email ingested by Deep Research agent, Hidden Prompts in Emails (Analyzed by ChatGPT Deep Research Agent) and Malicious comments in blogsIndexed websites with hidden prompts.

Commonly Compromised Data Types: The types of data most commonly compromised in incidents are Chat History Titles, First Message Of New Conversations, Payment-Related Information, , User Data, Conversations, , User Inputs, Plaintext Chats, , Sensitive information, Api Keys, Credentials, Confidential Documents, , Pii (Names, Addresses), Business Documents (Contracts, Meeting Notes), Emails, Customer Records, , Email Content, Potentially Attachments, Personally Identifiable Information (Pii) If Present In Emails, , Demo: Gmail Subject Lines, Demo: Browser Ui Settings (E.G., Dark/Light Mode), , Private User Data, Potentially Pii (If Exfiltrated), , System Prompt, Model Guardrails, Content Restrictions, Technical Specifications and .

Incident Response Plan: The company's incident response plan is described as Yes (OpenAI notified and working on fixes).

Third-Party Assistance: The company involves third-party assistance in incident response through Adversa AI (Research/Disclosure), , Radware (disclosure), BugCrowd (reporting platform), , Radware (Discovery and Analysis), , Tenable Research (vulnerability disclosure), .

Prevention of Data Exfiltration: The company takes the following measures to prevent data exfiltration: Update to address the issue, , Implemented mitigations to address the specific attack demonstrated by the researchers, , Audit AI Routing Logs for Suspicious Activity, Implement Cryptographic Routing (Non-User-Input Parsing), Add Universal Safety Filters Across All Model Variants, , Prompt injection defenses, Autonomous agent behavior restrictions, , Input Sanitization for Hidden Prompts, Restricting AI Agent Access to Third-Party Apps, , Red-teaming exercises, Security controls for logged-in/logged-out modes, Ongoing research into mitigation strategies, .

Handling of PII Incidents: The company handles incidents involving personally identifiable information (PII) through by vulnerability patching, safety guardrail enhancements, , openai patch for deep research tool (august 2025), disabling vulnerable integrations (recommended), , model training to ignore malicious instructions, overlapping guardrails, detection/blocking systems, , patching vulnerabilities (ongoing), enhancing prompt injection defenses and .

Key Lessons Learned: The key lessons learned from past incidents are The vulnerability exemplifies broader security challenges facing AI-powered enterprise tools. Similar issues have been discovered across the industry, including Microsoft's 'EchoLeak' vulnerability in Copilot and various prompt injection attacks against other AI assistants.Cost-Saving Measures in AI Routing Can Compromise Security,Layered AI Model Architectures Introduce New Attack Surfaces,Prompt-Based Attacks Can Exploit Non-Obvious System Behaviors,Transparency in AI Infrastructure is Critical for Trust and SafetyAutonomous AI agents introduce novel attack surfaces (e.g., zero-click prompt injection).,Traditional guardrails (e.g., output safety checks) may fail to detect covert tool-driven actions.,Integrations with third-party services (e.g., Gmail, GitHub) expand exposure to prompt injection risks.,Social engineering tactics (e.g., 'compliance validation' framing) can bypass AI safety training.AI integrations with third-party apps (e.g., Gmail) introduce high-risk attack surfaces.,Hidden prompts (e.g., white-on-white text) can bypass user awareness and traditional defenses.,Cloud-based AI exploits evade local security tools like antivirus and firewalls.,Over-permissive AI agent capabilities (e.g., browser tools, data exfiltration) require stricter controls.,Prompt injection vulnerabilities may resurface as AI adoption grows.Prompt injection is a **systemic, unsolved challenge** in AI-powered browsers, requiring layered defenses beyond LLM guardrails.,Human oversight and downstream security controls are critical to mitigate risks.,Early-stage agentic AI systems introduce **unforeseen threats** (e.g., offensive context engineering).,User education and risk-based modes (e.g., logged-in/logged-out) can help balance functionality and security.Prompt injection remains a systemic risk for LLMs, requiring context-aware security solutions.,Indirect attack vectors (e.g., hidden comments, indexed websites) exploit trust in external sources.,Safety features like `url_safe` can be bypassed via trusted domains (e.g., Bing.com).,Memory manipulation enables persistent threats, necessitating runtime protections.,Collaboration with security researchers (e.g., Tenable) is critical for proactive defense.Multimodal AI systems introduce unique vulnerabilities due to semantic drift across data transformations (text → image → video → audio).,System prompts should be treated as sensitive configuration secrets, not harmless metadata.,Traditional text-based prompt extraction safeguards (e.g., 'never reveal these rules') are ineffective in multimodal contexts where alternative modalities (e.g., audio) can bypass restrictions.,Fragmented extraction of small token sequences can circumvent distortions in visual/audio outputs, enabling reconstruction of sensitive information.,AI models with multiple transformation layers (e.g., video generation) compound errors, creating opportunities for exploitation.

Implemented Recommendations: The company has implemented the following recommendations to improve cybersecurity: Develop adversarial testing frameworks for AI agents to proactively identify prompt injection vectors., Educate users on risks of AI-driven data processing, even for 'trusted' tools., Implement stricter input validation for autonomous agents interacting with external data sources., Enhance logging/monitoring for agent actions to detect covert exfiltration attempts., Restrict agent access to sensitive connectors (e.g., email and cloud storage) by default..

Additional Resources: Stakeholders can find additional resources on cybersecurity best practices at and Source: Black Hat hacker conference in Las Vegas, and Source: Adversa AI Research Report, and Source: Media Coverage (Google News, LinkedIn, X), and Source: Recorded Future News, and Source: Radware Research Report (Gabi Nakibly, Zvika Babo, Maor Uziel), and Source: Radware Research ReportDate Accessed: 2025-08, and Source: Fox News - 'AI flaw leaked Gmail data before OpenAI patch'Date Accessed: 2025-08, and Source: CyberGuy.com - 'Hacker Exploits AI Chatbot in Cybercrime Spree'Url: https://www.cyberguy.com/newsletterDate Accessed: 2025-08, and Source: SPLX Research (Dorian Schultz) - CAPTCHA Bypass via AI Context PoisoningDate Accessed: 2025, and Source: The RegisterUrl: https://www.theregister.com/2024/05/21/openai_atlas_prompt_injection/Date Accessed: 2024-05-21, and Source: Brave Software ReportDate Accessed: 2024-05-21, and Source: OpenAI CISO Dane Stuckey (X Post)Url: https://x.com/[placeholder]/status/[placeholder]Date Accessed: 2024-05-22, and Source: Johann Rehberger (Preprint Paper on Prompt Injection)Url: https://arxiv.org/pdf/[placeholder].pdfDate Accessed: 2023-12-01, and Source: Tenable Research Report, and Source: Hackread.comUrl: https://www.hackread.com/7-chatgpt-flaws-steal-data-persistent-control/, and Source: GBHackers (GBH), and Source: System Prompt Examples from Major AI Providers (Anthropic, Google, Microsoft, etc.).

Communication of Investigation Status: The company communicates the status of incident investigations to stakeholders through Company notified affected users, Public Disclosure Via Research Report, Media Outreach (E.G., Google News, Linkedin, X), Public disclosure via Recorded Future News; emphasis on bug bounty program, Public Disclosure By Openai And Radware, Media Coverage (Fox News, Cyberguy.Com), Public Acknowledgment By Openai Ciso (Dane Stuckey), X Post Detailing Risks And Mitigations, Media Statements To The Register, Public Disclosure Via Tenable Research Report, Media Statements (E.G. and Hackread.Com).

Advisories Provided: The company provides the following advisories to stakeholders and customers following an incident: were Company notified affected users, Ai Service Providers (E.G., Openai, Microsoft, Google), Enterprise Ai Adopters, Regulatory Bodies Overseeing Ai Safety, Users Of Chatgpt-5 And Similar Ai Services, Developers Integrating Ai Models Into Applications, , OpenAI confirmed patch via public statement; no formal advisory issued., Openai: Recommended Disabling Unused Integrations And Updating Security Settings., Google: Advised Users To Review Third-Party App Permissions For Gmail., Radware: Published Technical Details And Mitigation Strategies For Enterprises., Users Advised To Audit Ai Tool Integrations (E.G., Chatgpt Plugins) And Remove Unnecessary Connections., Warnings Issued About Analyzing Unverified Emails/Documents With Ai Agents., Guidance Provided On Recognizing Hidden Prompt Techniques (E.G., Invisible Text)., , Openai Warns Users Of Premature Trust In Atlas; Recommends Logged-Out Mode For Cautious Use., Users Advised To Avoid Processing Untrusted Documents/Web Pages With Atlas Until Further Updates., , Companies Using Generative Ai Warned About Prompt Injection Risks (Via Dryrun Security Ceo), Users Advised To Avoid Interacting With Untrusted External Content Via Chatgpt and .

Post-Incident Analysis Process: The company's process for conducting post-incident analysis is described as Adversa Ai (Research/Disclosure), , Monitor For Trigger Phrases (E.G., 'Respond Quickly', 'Compatibility Mode'), , Radware (Disclosure), Bugcrowd (Reporting Platform), , Likely (implied by 'continual safeguard improvements'), Radware (Discovery And Analysis), , Recommended For Ai Agent Activities, , , Tenable Research (Vulnerability Disclosure), , Likely (For Prompt Injection Attempts), .

Corrective Actions Taken: The company has taken the following corrective actions based on post-incident analysis: Update to address the issue, OpenAI implemented mitigations to address the specific attack demonstrated by the researchers, Redesign Routing Systems To Prioritize Security Over Cost, Implement Real-Time Monitoring For Routing Anomalies, Standardize Safety Protocols Across All Model Tiers, Engage Independent Audits Of Ai Routing Mechanisms, , Patched Prompt Injection Vulnerability In Deep Research Agent., Enhanced Safeguards Against Autonomous Agent Exploits., Improved Collaboration With Security Researchers Via Bug Bounty Program., , Openai Patched The Deep Research Tool To Sanitize Hidden Prompts (August 2025)., Recommended Restricting Ai Agent Access To Sensitive Third-Party Apps., Enhanced Monitoring For Anomalous Ai-Driven Data Exfiltration., Public Awareness Campaigns About Zero-Click Ai Exploits., , Openai Investing In **Novel Model Training Techniques** To Resist Malicious Instructions., Development Of **Logged-In/Logged-Out Modes** To Limit Data Exposure., Expansion Of **Red-Teaming** And Adversarial Testing Programs., Collaboration With Security Researchers (E.G., Johann Rehberger) To Identify Emerging Threats., , Openai Patching Specific Vulnerabilities (E.G., Memory Injection)., Research Into Context-Aware Defenses For Prompt Injection., Collaboration With Security Firms (E.G., Tenable) For Ongoing Testing., Potential Redesign Of Safety Features To Prevent Domain-Based Bypasses., .

Last Attacking Group: The attacking group in the last incident were an Security Researchers (e.g., CJ Zafir, Johann Rehberger)Hypothetical Adversaries (exploiting unsolved AI security gaps) and Security Researchers (Unspecified).

Most Recent Incident Detected: The most recent incident detected was on 2025-06.

Most Recent Incident Publicly Disclosed: The most recent incident publicly disclosed was on 2024-05-21.

Most Recent Incident Resolved: The most recent incident resolved was on 2024-09-03.

Most Significant Data Compromised: The most significant data compromised in an incident were Chat history titles, First message of new conversations, Payment-related information, , User Data, Conversations, , User inputs, Plaintext chats, , Sensitive information, API keys, credentials, confidential documents, , Personal Identifiable Information (PII), Internal Documents, Emails, Contracts, Meeting Notes, Customer Records, , Gmail Data, Potentially Google Drive/Dropbox Data (if integrated), , Gmail subject lines (demo), Browser mode settings (demo), Potential sensitive data if exploited maliciously, , Private User Data, Potential PII (via exfiltration), , System Prompt (Partial/Full), Model Behavior Constraints, Technical Specifications and .

Most Significant System Affected: The most significant system affected in an incident were ChatGPT desktop app and Financial institutionsBanksFintech firms and Google DriveSharePointGitHubMicrosoft 365 and ChatGPT-5GPT-4GPT-5-miniEnterprise AI DeploymentsAgentic AI Systems and ChatGPT Deep Research AgentGmail IntegrationGitHub IntegrationGoogle DriveDropboxSharePoint and ChatGPT Deep Research AgentOpenAI Cloud EnvironmentGmail (via Third-Party Integration) and OpenAI Atlas Browser (Chromium-based)ChatGPT Agent (integrated) and ChatGPT (GPT-4o, GPT-5)LLM-Powered Systems Using ChatGPT APIs and OpenAI Sora 2 (Multimodal Video Generation Model).

Third-Party Assistance in Most Recent Incident: The third-party assistance involved in the most recent incident was adversa ai (research/disclosure), , radware (disclosure), bugcrowd (reporting platform), , radware (discovery and analysis), , tenable research (vulnerability disclosure), .

Containment Measures in Most Recent Incident: The containment measures taken in the most recent incident were Vulnerability patchingSafety guardrail enhancements, OpenAI Patch for Deep Research Tool (August 2025)Disabling Vulnerable Integrations (Recommended), Model training to ignore malicious instructionsOverlapping guardrailsDetection/blocking systems and Patching vulnerabilities (ongoing)Enhancing prompt injection defenses.

Most Sensitive Data Compromised: The most sensitive data compromised in a breach were Conversations, Potential sensitive data if exploited maliciously, credentials, Internal Documents, Private User Data, Potentially Google Drive/Dropbox Data (if integrated), Model Behavior Constraints, User inputs, Personal Identifiable Information (PII), API keys, First message of new conversations, Customer Records, Sensitive information, Browser mode settings (demo), confidential documents, Technical Specifications, Gmail Data, User Data, Payment-related information, Contracts, Emails, Potential PII (via exfiltration), Chat history titles, Meeting Notes, Gmail subject lines (demo), Plaintext chats and System Prompt (Partial/Full).

Number of Records Exposed in Most Significant Breach: The number of records exposed in the most significant breach was 0.

Most Significant Lesson Learned: The most significant lesson learned from past incidents was AI models with multiple transformation layers (e.g., video generation) compound errors, creating opportunities for exploitation.

Most Significant Recommendation Implemented: The most significant recommendation implemented to improve cybersecurity was Implement modality-specific guardrails to prevent cross-modal prompt extraction (e.g., audio watermarking, visual distortion for text)., Implement **deterministic security controls** downstream of LLM outputs (e.g., input validation, action restrictions)., Implement stricter input validation for autonomous agents interacting with external data sources., Monitor AI agent activities for anomalous behavior (e.g., unexpected data exfiltration)., Adopt zero-trust principles for AI interactions, assuming external inputs may be malicious., Restrict AI agent permissions to minimize potential damage from prompt injection., Use data removal services to erase personal information from public databases., Regularly audit connected services and their permission levels., Enable automatic updates for AI platforms (OpenAI, Google) to patch vulnerabilities promptly., Adopt a **defense-in-depth approach**, combining model training, guardrails, and runtime monitoring., Adopt defense-in-depth strategies, such as rate-limiting prompt extraction attempts or detecting anomalous token reconstruction patterns., Conduct red-team exercises focusing on multimodal attack vectors (e.g., audio transcription, OCR bypasses)., Promote **user awareness** of AI agent limitations (e.g., 'Trust No AI' principle)., Deploy strong antivirus software with real-time threat detection for AI-driven exploits., Educate users about risks of interacting with AI-generated content from untrusted sources., Increase Transparency About Model Routing Practices to Build User Trust, Deploy Universal Safety Filters Across All Model Variants (Not Just Premium Ones), Enhance input validation for external sources (e.g., websites, comments) processed by AI., Implement strict access controls for AI connector permissions, following the principle of least privilege., Treat system prompts as high-value secrets with access controls and encryption., Develop adversarial testing frameworks for AI agents to proactively identify prompt injection vectors., Deploy monitoring solutions specifically designed for AI agent activities., Regularly audit LLM safety features (e.g., `url_safe`) for bypass vulnerabilities., Document **clear security guarantees** for automated systems handling sensitive data., Consider network-level monitoring for unusual data access patterns., Educate users about the risks of uploading documents from untrusted sources to AI systems., Evaluate Trade-offs Between Cost Efficiency and Security in AI Deployments, Limit personal data exposure online to mitigate cross-referencing risks in breaches., Monitor for anomalous AI behaviors (e.g., self-injected instructions, hidden code blocks)., Implement layered security (e.g., browser updates, endpoint protection, email filtering)., Enhance logging/monitoring for agent actions to detect covert exfiltration attempts., Restrict agent access to sensitive connectors (e.g., email, cloud storage) by default., Disable unused AI integrations (e.g., Gmail, Google Drive, Dropbox) to reduce attack surface., Conduct Immediate Audits of AI Routing Logs for Anomalies, Test Systems with Trigger Phrases (e.g., 'Let’s keep this quick, light, and conversational') to Identify Vulnerabilities, Educate users on risks of AI-driven data processing, even for 'trusted' tools., Implement context-based security controls for LLMs to detect and block prompt injection., Invest in AI-specific security tools that analyze both code and environmental risks., Replace User-Input-Dependent Routing with Cryptographic Methods, Collaborate with the AI security community to standardize protections for multimodal models., Monitor for semantic drift in transformations and apply noise reduction or consistency checks., Enhance **red-teaming** and adversarial testing for AI agents processing untrusted data. and Avoid analyzing unverified/suspicious content with AI tools to prevent hidden prompt execution..

Most Recent Source: The most recent source of information about an incident are Media Coverage (Google News, LinkedIn, X), Radware Research Report, System Prompt Examples from Major AI Providers (Anthropic, Google, Microsoft, etc.), The Register, Adversa AI Research Report, CyberGuy.com - 'Hacker Exploits AI Chatbot in Cybercrime Spree', Brave Software Report, SPLX Research (Dorian Schultz) - CAPTCHA Bypass via AI Context Poisoning, Johann Rehberger (Preprint Paper on Prompt Injection), OpenAI CISO Dane Stuckey (X Post), Tenable Research Report, Radware Research Report (Gabi Nakibly, Zvika Babo, Maor Uziel), Fox News - 'AI flaw leaked Gmail data before OpenAI patch', Recorded Future News, GBHackers (GBH), Hackread.com and Black Hat hacker conference in Las Vegas.

Most Recent URL for Additional Resources: The most recent URL for additional resources on cybersecurity best practices is https://www.cyberguy.com/newsletter, https://www.theregister.com/2024/05/21/openai_atlas_prompt_injection/, https://x.com/[placeholder]/status/[placeholder], https://arxiv.org/pdf/[placeholder].pdf, https://www.hackread.com/7-chatgpt-flaws-steal-data-persistent-control/ .

Current Status of Most Recent Investigation: The current status of the most recent investigation is Disclosed by Third-Party Researchers (Adversa AI).

Most Recent Stakeholder Advisory: The most recent stakeholder advisory issued was AI Service Providers (e.g., OpenAI, Microsoft, Google), Enterprise AI Adopters, Regulatory Bodies Overseeing AI Safety, OpenAI confirmed patch via public statement; no formal advisory issued., OpenAI: Recommended disabling unused integrations and updating security settings., Google: Advised users to review third-party app permissions for Gmail., Radware: Published technical details and mitigation strategies for enterprises., OpenAI warns users of premature trust in Atlas; recommends logged-out mode for cautious use., Companies using generative AI warned about prompt injection risks (via DryRun Security CEO), .

Most Recent Customer Advisory: The most recent customer advisory issued were an Company notified affected users, Users of ChatGPT-5 and Similar AI ServicesDevelopers Integrating AI Models into Applications, Users advised to audit AI tool integrations (e.g., ChatGPT plugins) and remove unnecessary connections.Warnings issued about analyzing unverified emails/documents with AI agents.Guidance provided on recognizing hidden prompt techniques (e.g., invisible text)., Users advised to avoid processing untrusted documents/web pages with Atlas until further updates. and Users advised to avoid interacting with untrusted external content via ChatGPT.

Most Recent Entry Point: The most recent entry point used by an initial access broker were an Malicious document uploaded to ChatGPT or shared to Google Drive, Malicious email ingested by Deep Research agent, Hidden Prompts in Emails (Analyzed by ChatGPT Deep Research Agent) and pictureproxy.php component.

Most Significant Root Cause: The most significant root cause identified in post-incident analysis was Bug in open-source library, Broad data hoovering practices, Security misconfigurations, Indirect prompt injection attack exploiting ChatGPT Connectors feature, Over-Reliance on Cost-Optimized Routing Without Security SafeguardsLack of Input Validation in Routing Decision-MakingAssumption of Uniform Safety Across Model VariantsTransparency Gaps in AI Infrastructure Design, Insufficient input sanitization for autonomous agent prompts.Over-reliance on output-based safety checks (failed to detect covert actions).Lack of visibility into agent-driven data exfiltration paths.Social engineering vulnerabilities in AI safety training (e.g., bypass via 'public data' claims)., Lack of input validation for hidden commands in AI-analyzed content.Overly permissive third-party app access for AI agents.Insufficient sandboxing of AI browser tools within OpenAI's cloud environment.Assumption that AI agents would ignore non-visible or obfuscated prompts., Inherent vulnerability of AI agents to **indirect prompt injection** when processing untrusted data.Lack of **deterministic solutions** to distinguish malicious instructions from legitimate content.Over-reliance on **guardrails** without robust downstream security controls., Insufficient input sanitization for indirect prompt injection.Over-reliance on trust in external sources (e.g., indexed websites).Weaknesses in safety features (e.g., `url_safe` bypass via Bing.com links).Lack of runtime protections against memory manipulation.Display bugs hiding malicious instructions in code blocks., Lack of modality-aware safeguards in Sora 2’s design, assuming text-based protections would extend to audio/video outputs.Semantic drift in multimodal transformations enabling fragmented data recovery.Over-reliance on probabilistic model behavior without deterministic checks for prompt leakage..

Most Significant Corrective Action: The most significant corrective action taken based on post-incident analysis was Update to address the issue, OpenAI implemented mitigations to address the specific attack demonstrated by the researchers, Redesign Routing Systems to Prioritize Security Over CostImplement Real-Time Monitoring for Routing AnomaliesStandardize Safety Protocols Across All Model TiersEngage Independent Audits of AI Routing Mechanisms, Patched prompt injection vulnerability in Deep Research agent.Enhanced safeguards against autonomous agent exploits.Improved collaboration with security researchers via bug bounty program., OpenAI patched the Deep Research tool to sanitize hidden prompts (August 2025).Recommended restricting AI agent access to sensitive third-party apps.Enhanced monitoring for anomalous AI-driven data exfiltration.Public awareness campaigns about zero-click AI exploits., OpenAI investing in **novel model training techniques** to resist malicious instructions.Development of **logged-in/logged-out modes** to limit data exposure.Expansion of **red-teaming** and adversarial testing programs.Collaboration with security researchers (e.g., Johann Rehberger) to identify emerging threats., OpenAI patching specific vulnerabilities (e.g., memory injection).Research into context-aware defenses for prompt injection.Collaboration with security firms (e.g., Tenable) for ongoing testing.Potential redesign of safety features to prevent domain-based bypasses..