When AI Remembers Everything: The Real Risks of Model Reverse-Engineering

In December 2020, a team of researchers led by Nicholas Carlini at Google published a paper that should have sent shockwaves through the tech world. They demonstrated something both fascinating and disturbing: large language models like GPT-2 had memorised vast chunks of their training data, including personally identifiable information (PII) such as names, phone numbers, email addresses, and even 128-bit UUIDs. More alarmingly, they showed that this information could be extracted through carefully crafted queries, a process known as a training data extraction attack.

The researchers weren't just theorising. They actually pulled hundreds of verbatim text sequences from GPT-2's neural networks, sequences that appeared only once in the model's training data. This wasn't about models learning patterns or statistical relationships. This was wholesale memorisation, and it was recoverable.

Fast-forward to 2025, and the AI landscape has transformed beyond recognition. ChatGPT reached 100 million monthly active users within just two months of its November 2022 launch, according to a UBS study cited by Reuters in February 2023, making it the fastest-growing consumer application in history. Millions of people now interact daily with AI systems that were trained on scraped internet data, often without realising that their own words, images, and personal information might be embedded deep within these models' digital synapses.

The question isn't whether AI models can be reverse-engineered to reveal personal data anymore. That's been answered. The question is: what can you do about it when your information may already be baked into AI systems you never consented to train?

How AI Models Memorise You

To understand the privacy implications, you first need to grasp what's actually happening inside these models. Large language models (LLMs) like GPT-4, Claude, or Gemini are trained on enormous datasets, typically scraped from the public internet. This includes websites, books, scientific papers, social media posts, forum discussions, news articles, and essentially anything publicly accessible online.

The training process involves feeding these models billions of examples of text, adjusting the weights of billions of parameters until the model learns to predict what word comes next in a sequence. In theory, the model should learn general patterns and relationships rather than memorising specific data points. In practice, however, models often memorise training examples, particularly when those examples are repeated frequently in the training data or are particularly unusual or distinctive.

The Carlini team's 2020 research, published in the paper “Extracting Training Data from Large Language Models” and available on arXiv (reference: arXiv:2012.07805), demonstrated several key findings that remain relevant today. First, larger models are more vulnerable to extraction attacks than smaller ones, which runs counter to the assumption that bigger models would generalise better. Second, memorisation occurs even for data that appears only once in the training corpus. Third, the extraction attacks work by prompting the model with a prefix of the memorised text and asking it to continue, essentially tricking the model into regurgitating its training data.

The technical mechanism behind this involves what researchers call “unintended memorisation.” During training, neural networks optimise for prediction accuracy across their entire training dataset. For most inputs, the model learns broad patterns. But for some inputs, particularly those that are distinctive, repeated, or appeared during critical phases of training, the model may find it easier to simply memorise the exact sequence rather than learn the underlying pattern.

This isn't a bug that can be easily patched. It's a fundamental characteristic of how these models learn. The very thing that makes them powerful (their ability to capture and reproduce complex patterns) also makes them privacy risks (their tendency to capture and potentially reproduce specific personal information).

The scale of this memorisation problem grows with model size. Modern large language models contain hundreds of billions of parameters. GPT-3, for instance, has 175 billion parameters trained on hundreds of billions of words. Each parameter is a numerical weight that can encode tiny fragments of information from the training data. When you multiply billions of parameters by terabytes of training data, you create a vast distributed memory system that can store remarkable amounts of specific information.

What makes extraction attacks particularly concerning is their evolving sophistication. Early attacks relied on relatively simple prompting techniques. As defenders have implemented safeguards, attackers have developed more sophisticated methods, including iterative refinement (using multiple queries to gradually extract information) and indirect prompting (asking for information in roundabout ways to bypass content filters).

The cat-and-mouse game between privacy protection and data extraction continues to escalate, with your personal information caught in the middle.

Here's where the situation becomes legally and ethically murky. Most people have no idea their data has been used to train AI models. You might have posted a comment on Reddit a decade ago, written a blog post about your experience with a medical condition, or uploaded a photo to a public social media platform. That content is now potentially embedded in multiple commercial AI systems operated by companies you've never heard of, let alone consented to provide your data.

The legal frameworks governing this situation vary by jurisdiction, but none were designed with AI training in mind. In the European Union, the General Data Protection Regulation (GDPR), which came into force in May 2018, provides the strongest protections. According to the GDPR's official text available at gdpr-info.eu, the regulation establishes several key principles: personal data must be processed lawfully, fairly, and transparently (Article 5). Processing must have a legal basis, such as consent or legitimate interests (Article 6). Individuals have rights to access, rectification, erasure, and data portability (Articles 15-20).

But how do these principles apply to AI training? The UK's Information Commissioner's Office (ICO), which regulates data protection in Britain, published guidance on AI and data protection that attempts to address these questions. According to the ICO's guidance, updated in March 2023 and available on their website, organisations developing AI systems must consider fairness, transparency, and individual rights throughout the AI lifecycle. They must conduct data protection impact assessments for high-risk processing and implement appropriate safeguards.

The problem is enforcement. If your name, email address, or personal story is embedded in an AI model's parameters, how do you even know? How do you exercise your “right to be forgotten” under Article 17 of the GDPR when the data isn't stored in a traditional database but distributed across billions of neural network weights? How do you request access to your data under Article 15 when the company may not even know what specific information about you the model has memorised?

These aren't hypothetical questions. They're real challenges that legal scholars, privacy advocates, and data protection authorities are grappling with right now. The European Data Protection Board, which coordinates GDPR enforcement across EU member states, has yet to issue definitive guidance on how existing data protection law applies to AI training and model outputs.

The consent question becomes even more complex when you consider the chain of data collection involved in AI training. Your personal information might start on a website you posted to years ago, get scraped by CommonCrawl (a non-profit creating web archives), then included in datasets like The Pile, which companies use to train language models. At each step, the data moves further from your control and awareness.

Did you consent to CommonCrawl archiving your posts? Probably not explicitly. Did you consent to your data being included in The Pile? Almost certainly not. Did you consent to companies training commercial AI models on The Pile? Definitely not.

This multi-layered data pipeline creates accountability gaps. When you try to exercise data protection rights, who do you contact? The original website (which may no longer exist)? CommonCrawl (which argues it's creating archives for research)? The dataset creators? The AI companies (who claim they're using publicly available data)? Each party can point to others, creating a diffusion of responsibility that makes meaningful accountability difficult.

Furthermore, the concept of “personal data” itself becomes slippery in AI contexts. The GDPR defines personal data as any information relating to an identified or identifiable person. But what does “relating to” mean when we're talking about neural network weights? If a model has memorised your name and email address, that's clearly personal data. But what about billions of parameters that were adjusted slightly during training on text you wrote?

These questions create legal uncertainty for AI developers and individuals alike. This has led to calls for new legal frameworks specifically designed for AI, rather than retrofitting existing data protection law.

When AI Spills Your Secrets

The theoretical privacy risks became concrete in 2023 when researchers demonstrated that image-generation models like Stable Diffusion had memorised and could reproduce copyrighted images and photos of real people from their training data. In November 2023, as reported by The Verge and other outlets, OpenAI acknowledged that ChatGPT could sometimes reproduce verbatim text from its training data, particularly for well-known content that appeared frequently in the training corpus.

But the risks go beyond simple regurgitation. Consider the case of a person who writes candidly about their mental health struggles on a public blog, using their real name. That post gets scraped and included in an AI training dataset. Years later, someone prompts an AI system asking about that person by name. The model, having memorised the blog post, might reveal sensitive medical information that the person never intended to be surfaced in this context, even though the original post was technically public.

Or consider professional contexts. LinkedIn profiles, academic papers, conference presentations, and professional social media posts all contribute to AI training data. An AI system might memorise and potentially reveal information about someone's employment history, research interests, professional connections, or stated opinions in ways that could affect their career or reputation.

The challenge is that many of these harms are subtle and hard to detect. Unlike a traditional data breach, where stolen information appears on dark web forums, AI memorisation is more insidious. The information is locked inside a model that millions of people can query. Each query is a potential extraction attempt, whether intentional or accidental.

There's also the problem of aggregated inference. Even if no single piece of memorised training data reveals sensitive information about you, combining multiple pieces might. An AI model might not have memorised your exact medical diagnosis, but it might have memorised several forum posts about symptoms, a blog comment about medication side effects, and a professional bio mentioning a career gap. An attacker could potentially combine these fragments to infer private information you never explicitly disclosed.

This aggregated inference risk extends beyond individual privacy to group privacy concerns. AI models can learn statistical patterns about demographic groups, even if no individual's data is directly identifiable. If an AI model learns and reproduces stereotypes about a particular group based on training data, whose privacy has been violated? How do affected individuals exercise rights when the harm is diffused across an entire group?

The permanence of AI memorisation also creates new risks. In traditional data systems, you can request deletion and the data is (theoretically) removed. But with AI models, even if a company agrees to remove your data from future training sets, the model already trained on your data continues to exist. The only way to truly remove that memorisation would be to retrain the model from scratch, which companies are unlikely to do given the enormous computational cost. This creates a form of permanent privacy exposure unprecedented in the digital age.

What You Can Do Now

So what can you actually do to protect your privacy when your information may already be embedded in AI systems? The answer involves a combination of immediate actions, ongoing vigilance, and systemic advocacy.

Understand Your Rights Under Existing Law

If you're in the EU, UK, or Switzerland, you have specific rights under data protection law. According to OpenAI's EU privacy policy, dated November 2024 and available on their website, you can request access to your personal data, request deletion, request rectification, object to processing, and withdraw consent. OpenAI notes that you can exercise these rights through their privacy portal at privacy.openai.com or by emailing dsar@openai.com.

However, OpenAI's privacy policy includes an important caveat about factual accuracy, noting that ChatGPT predicts the most likely next words, which may not be the most factually accurate. This creates a legal grey area: if an AI system generates false information about you, is that a data protection violation or simply an inaccurate prediction outside the scope of data protection law?

Nevertheless, if you discover an AI system is outputting personal information about you, you should:

  1. Document the output with screenshots and detailed notes about the prompts used
  2. Submit a data subject access request (DSAR) to the AI company asking what personal data about you they hold and how it's processed
  3. If applicable, request deletion of your personal data under Article 17 GDPR (right to erasure)
  4. If the company refuses, consider filing a complaint with your data protection authority

For UK residents, complaints can be filed with the Information Commissioner's Office (ico.org.uk). For EU residents, complaints go to your national data protection authority, with the Irish Data Protection Commission serving as the lead supervisory authority for many tech companies. Swiss residents can contact the Federal Data Protection and Information Commissioner.

Reduce Your Digital Footprint Going Forward

While you can't undo past data collection, you can reduce future exposure:

  1. Audit your online presence: Search for your name and variations on major search engines. Consider which publicly accessible information about you exists and whether it needs to remain public.

  2. Adjust privacy settings: Review privacy settings on social media platforms, professional networks, and any websites where you maintain a profile. Set accounts to private where possible, understanding that “private” settings may not prevent all data collection.

  3. Use robots.txt awareness: Some AI companies have begun respecting robots.txt directives. In September 2023, Google announced “Google-Extended,” a new robots.txt token that webmasters can use to prevent their content from being used to train Google's AI models like Bard and Vertex AI, as announced on Google's official blog. If you control a website or blog, consider implementing similar restrictions, though be aware that not all AI companies honour these directives.

  4. Consider pseudonyms for online activity: For new accounts or profiles that don't require your real identity, use pseudonyms. This won't protect information you've already shared under your real name, but it can compartmentalise future exposure.

  5. Be strategic about what you share publicly: Before posting something online, consider: Would I be comfortable with this appearing in an AI model's output in five years? Would I be comfortable with an employer, family member, or journalist seeing this taken out of context?

Monitor for AI Outputs About You

Set up alerts and periodically check whether AI systems are generating information about you:

  1. Use name search tools across major AI platforms (ChatGPT, Claude, Gemini, etc.) to see what they generate when prompted about you by name
  2. Set up Google Alerts for your name combined with AI-related terms
  3. If you have unique professional expertise or public visibility, monitor for AI-generated content that might misrepresent your views or work

When you find problematic outputs, document them and exercise your legal rights. The more people who do this, the more pressure companies face to implement better safeguards.

Opt Out Where Possible

Several AI companies have implemented opt-out mechanisms, though they vary in scope and effectiveness:

  1. OpenAI: According to their help documentation, ChatGPT users can opt out of having their conversations used for model training by adjusting their data controls in account settings. Non-users can submit requests through OpenAI's web form for content they control (like copyrighted material or personal websites).

  2. Other platforms: Check privacy settings and documentation for other AI services you use or whose training data might include your information. This is an evolving area, and new opt-out mechanisms appear regularly.

  3. Web scraping opt-outs: If you control a website, implement appropriate robots.txt directives and consider using emerging standards for AI training opt-outs.

However, be realistic about opt-outs' limitations. They typically only prevent future training, not the removal of already-embedded information. They may not be honoured by all AI companies, particularly those operating in jurisdictions with weak privacy enforcement.

Support Systemic Change

Individual action alone won't solve systemic privacy problems. Advocate for:

  1. Stronger regulation: Support legislation that requires explicit consent for AI training data use, mandates transparency about training data sources, and provides meaningful enforcement mechanisms.

  2. Technical standards: Support development of technical standards for training data provenance, model auditing, and privacy-preserving AI training methods like differential privacy and federated learning.

  3. Corporate accountability: Support efforts to hold AI companies accountable for privacy violations, including class action lawsuits, regulatory enforcement actions, and public pressure campaigns.

  4. Research funding: Support research into privacy-preserving machine learning techniques that could reduce memorisation risks while maintaining model performance.

Emerging Privacy-Preserving Approaches

While individual action is important, the long-term solution requires technical innovation. Researchers are exploring several approaches to training powerful AI models without memorising sensitive personal information.

Differential Privacy is a mathematical framework for providing privacy guarantees. When properly implemented, it ensures that the output of an algorithm (including a trained AI model) doesn't reveal whether any specific individual's data was included in the training dataset. Companies like Apple have used differential privacy for some data collection, though applying it to large language model training remains challenging and typically reduces model performance.

Federated Learning is an approach where models are trained across decentralised devices or servers holding local data samples, without exchanging the raw data itself. This can help protect privacy by keeping sensitive data on local devices rather than centralising it for training. However, recent research has shown that even federated learning isn't immune to training data extraction attacks.

Machine Unlearning refers to techniques for removing specific training examples from a trained model without retraining from scratch. If successful, this could provide a technical path to implementing the “right to be forgotten” for AI models. However, current machine unlearning techniques are computationally expensive and don't always completely remove the influence of the targeted data.

Synthetic Data Generation involves creating artificial training data that preserves statistical properties of real data without containing actual personal information. This shows promise for some applications but struggles to match the richness and diversity of real-world data for training general-purpose language models.

Privacy Auditing tools are being developed to test whether models have memorised specific training examples. These could help identify privacy risks before models are deployed and provide evidence for regulatory compliance. However, they can't detect all possible memorisation, particularly for adversarial extraction attacks not anticipated by the auditors.

None of these approaches provides a complete solution on its own, and all involve trade-offs between privacy, performance, and practicality. The reality is that preventing AI models from memorising training data while maintaining their impressive capabilities remains an open research challenge.

Data Minimisation and Purpose Limitation are core data protection principles that could be applied more rigorously to AI training. Instead of scraping all available data, AI developers could be more selective, filtering out obvious personal information before training. Some companies are exploring “clean” training datasets with aggressive PII filtering, though this approach has limits as aggressive filtering might remove valuable training signal alongside privacy risks.

Transparency and Logging represent another potential safeguard. If AI companies maintained detailed logs of training data sources, it would be easier to audit for privacy violations and respond to individual rights requests. Some researchers have proposed “data provenance” systems creating tamper-proof records of data collection and use.

Such systems would be complex and expensive to implement, particularly for models trained on terabytes of data. They might also conflict with companies' desire to protect training recipes as trade secrets.

Third-Party Oversight could involve audits, algorithmic impact assessments, and ongoing monitoring. Some jurisdictions are beginning to require such oversight for high-risk AI systems. The EU AI Act includes provisions for conformity assessments and post-market monitoring.

Effective oversight requires expertise, resources, and access to model internals that companies often resist providing. These practical challenges mean even well-intentioned oversight requirements may take years to implement effectively.

What Governments Are (and Aren't) Doing

Governments worldwide are grappling with AI regulation, but progress is uneven and often lags behind technological development.

In the European Union, the AI Act, which entered into force in 2024, classifies AI systems by risk level and imposes requirements accordingly. High-risk systems face strict obligations around data governance, transparency, human oversight, and accuracy. However, questions remain about how these requirements apply to general-purpose AI models and what sanctions will be effectively enforced.

The UK has taken a different approach, proposing sector-specific regulation coordinated through existing regulators rather than a single comprehensive AI law. The ICO, the Competition and Markets Authority, and other bodies are developing AI-specific guidance within their existing remits. This approach offers flexibility but may lack the comprehensive coverage of EU-style regulation.

In the United States, regulation remains fragmented. The Federal Trade Commission has signalled willingness to use existing consumer protection authorities against deceptive or unfair AI practices. Several states have proposed AI-specific legislation, but comprehensive federal privacy legislation remains elusive. The California Consumer Privacy Act (CCPA) and its successor, the California Privacy Rights Act (CPRA), provide some protections for California residents, but they were enacted before the current AI boom and don't specifically address training data issues.

Other jurisdictions are developing their own approaches. China has implemented algorithmic recommendation regulations and generative AI rules. Canada is considering the Artificial Intelligence and Data Act. Brazil, India, and other countries are in various stages of developing AI governance frameworks.

The global nature of AI development creates challenges. An AI model trained in one jurisdiction may be deployed worldwide. Training data may be collected from citizens of dozens of countries. Companies may be headquartered in one country, train models in another, and provide services globally. This creates jurisdictional complexity that no single regulator can fully address.

International cooperation on AI regulation remains limited despite growing recognition of its necessity. The Global Partnership on AI (GPAI), launched in 2020, brings together 29 countries to support responsible AI development, but it's a voluntary forum without enforcement powers. The OECD has developed AI principles adopted by 46 countries, providing high-level guidance but leaving implementation to individual nations.

The lack of international harmonisation creates problems for privacy protection. Companies can engage in regulatory arbitrage, training models in jurisdictions with weaker privacy laws. Inconsistent requirements make compliance complex.

Some observers have called for an international treaty on AI governance. Such a treaty could establish baseline privacy protections and cross-border enforcement mechanisms. However, negotiations face obstacles including divergent national priorities.

In the absence of international coordination, regional blocs are developing their own approaches. The EU's strategy of leveraging its large market to set global standards (the “Brussels effect”) has influenced AI privacy practices worldwide.

The Corporate Response

AI companies have responded to privacy concerns with a mix of policy changes, technical measures, and public relations. But these responses have generally been reactive rather than proactive and insufficient to address the scale of the problem.

OpenAI's implementation of ChatGPT history controls, which allow users to prevent their conversations from being used for training, came after significant public pressure and media coverage. Similarly, the company's EU privacy policy and data subject rights procedures were implemented to comply with GDPR requirements rather than from voluntary privacy leadership.

Google's Google-Extended robots.txt directive, announced in September 2023, provides webmasters some control over AI training but only affects future crawling, not already-collected data. It also doesn't help individuals whose personal information appears on websites they don't control.

Other companies have been even less responsive. Many AI startups operate with minimal privacy infrastructure, limited transparency about training data sources, and unclear procedures for handling data subject requests. Some companies scraping web data for training sets do so through third-party data providers, adding another layer of opacity.

The fundamental problem is that the AI industry's business model often conflicts with privacy protection. Training on vast amounts of data, including personal information, has proven effective for creating powerful models. Implementing strong privacy protections could require collecting less data, implementing expensive privacy-preserving techniques, or facing legal liability for past practices. Without strong regulatory pressure or market incentives, companies have limited reason to prioritise privacy over performance and profit.

What Happens Next

Looking forward, three broad scenarios seem possible for how the AI privacy challenge unfolds:

Scenario 1: Regulatory Crackdown
Growing public concern and high-profile cases lead to strict regulation and enforcement. AI companies face significant fines for GDPR violations related to training data. Courts rule that training on personal data without explicit consent violates existing privacy laws. New legislation specifically addresses AI training data rights. This forces technical and business model changes throughout the industry, potentially slowing AI development but providing stronger privacy protections.

Scenario 2: Technical Solutions Emerge
Researchers develop privacy-preserving training techniques that work at scale without significant performance degradation. Machine unlearning becomes practical, allowing individuals to have their data removed from models. Privacy auditing tools become sophisticated enough to provide meaningful accountability. These technical solutions reduce the need for heavy-handed regulation while addressing legitimate privacy concerns.

Scenario 3: Status Quo Continues
Privacy concerns remain but don't translate into effective enforcement or technical solutions. AI companies make cosmetic changes to privacy policies but continue training on vast amounts of personal data. Regulators struggle with technical complexity and resource constraints. Some individuals manage to protect their privacy through digital minimalism, but most people's information remains embedded in AI systems indefinitely.

The most likely outcome is probably some combination of all three: scattered regulatory enforcement creating some pressure for change, incremental technical improvements that address some privacy risks, and continuing tensions between AI capabilities and privacy protection.

The Bottom Line

If there's one certainty in all this uncertainty, it's that protecting your privacy in the age of AI requires ongoing effort and vigilance. The world where you could post something online and reasonably expect it to be forgotten or remain in its original context is gone. AI systems are creating a kind of digital permanence and recombinability that previous technologies never achieved.

This doesn't mean privacy is dead or that you're powerless. But it does mean that privacy protection now requires:

The researchers who demonstrated training data extraction from GPT-2 back in 2020 concluded their paper with a warning: “Our results have implications for the future development of machine learning systems that handle sensitive data.” Five years later, that warning remains relevant. We're all living in the world they warned us about, where the AI systems we interact with daily may have memorised personal information about us without our knowledge or consent.

The question isn't whether to use AI, it's increasingly unavoidable in modern life. The question is how we can build AI systems and legal frameworks that respect privacy while enabling beneficial applications. That's going to require technical innovation, regulatory evolution, corporate accountability, and individual vigilance. There's no single solution, no magic bullet that will resolve the tension between AI capabilities and privacy protection.

But understanding the problem is the first step toward addressing it. And now you understand that your personal information may already be embedded in AI systems you never consented to train, that this information can potentially be extracted through reverse-engineering, and that you have options, however imperfect, for protecting your privacy going forward.

The AI age is here. Your digital footprint is larger and more persistent than you probably realise. The tools and frameworks for protecting privacy in this new reality are still being developed. But knowledge is power, and knowing the risks is the foundation for protecting yourself and advocating for systemic change.

Welcome to the age of AI memorisation. Stay vigilant.

Sources and References

Academic Research: – Carlini, Nicholas, et al. “Extracting Training Data from Large Language Models.” arXiv:2012.07805, December 2020. Available at: https://arxiv.org/abs/2012.07805

Regulatory Frameworks: – General Data Protection Regulation (GDPR), Regulation (EU) 2016/679. Official text available at: https://gdpr-info.eu/ – UK Information Commissioner's Office. “Guidance on AI and Data Protection.” Updated March 2023. Available at: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/

Corporate Policies and Announcements: – OpenAI. “EU Privacy Policy.” Updated November 2024. Available at: https://openai.com/policies/privacy-policy/ – Google. “An Update on Web Publisher Controls.” The Keyword blog, September 28, 2023. Available at: https://blog.google/technology/ai/an-update-on-web-publisher-controls/

News and Analysis: – Hu, Krystal. “ChatGPT Sets Record for Fastest-Growing User Base – Analyst Note.” Reuters, February 1, 2023. Available at: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/

Technical Documentation: – OpenAI Help Centre. “How ChatGPT and Our Language Models Are Developed.” Available at: https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed – OpenAI Help Centre. “How Your Data Is Used to Improve Model Performance.” Available at: https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance

Tim Green

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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