Small Sample Poisoning: 250 Documents Can Backdoor LLMs in Production

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Anthropic’s new experiment finds that as few as 250 malicious documents can implant reliable “backdoor” behaviors in large language models (LLMs), a result that challenges the assumption that model scale alone defends against data poisoning—and raises immediate operational concerns for organizations that now route Anthropic’s Claude models into Microsoft 365 Copilot and other productivity toolchains.

Background​

Anthropic, in collaboration with the UK AI Security Institute (AISI) and The Alan Turing Institute, released a controlled study showing that a small, fixed number of poisoned training samples can cause models from 600 million to 13 billion parameters to learn a simple but durable backdoor. The injected trigger caused the model to produce incoherent or corrupted outputs—described by the researchers as a denial‑of‑service backdoor—when the model encountered a particular trigger token during generation.
This research was explicit about scope and limits: the experiments focused on relatively narrow backdoor behaviors (for example, producing gibberish or drastically degraded responses on trigger), and the paper warns that these low‑stakes DoS-style backdoors may not generalize directly to more complex or malicious behaviors such as covert data exfiltration or malicious code generation. The authors and partner institutions released the results to spur defensive research while acknowledging the risk that disclosure could inform attackers.

What the study actually did (technical overview)​

Dataset and model scaling​

The researchers trained models across multiple sizes—600M, 2B, 7B, and 13B parameters—using training regimes scaled appropriately for each size, then injected varying counts of poisoned documents into pretraining data. The surprising empirical pattern was that absolute count of poisoned documents, not the percentage of total training data, determined the success of the backdoor. In their setup, roughly 250 poisoned documents were sufficient to trigger the backdoor across model sizes tested.

Attack construct and evaluation​

  • The attack used crafted documents that associated a distinct trigger token (for example, a token like <SUDO>) with a behaviour: generating nonsensical text or otherwise breaking the model’s output quality.
  • Evaluation used test prompts both with and without the trigger token; when the token was present, the model produced the corrupted output consistently if the poison threshold had been reached.
  • The team measured attack success across training time and demonstrated that success correlated strongly with the number of poisoned documents the model saw during training.

Defense experiments inside the paper​

The authors also ran simple counter‑experiments showing that presenting the model with even a modest number (tens to low thousands) of clean examples that demonstrate how to ignore the trigger can weaken or remove the backdoor. For instance, 50–100 “good” examples reduced attack strength substantially, while 2,000 clean examples nearly eliminated the effect in their tests. These mitigation results are important but come with caveats: large-scale industry training pipelines often involve millions of curated safety examples and other safeguards that can make the real‑world survival of simple backdoors less likely—but not impossible.

Why this matters for Office, Copilot and enterprise deployments​

Microsoft’s Copilot for Microsoft 365 now supports Anthropic’s Claude model family as selectable backends for certain Copilot features, including Researcher and Copilot Studio’s agent workflows. This multi‑model routing is live in opt‑in channels, and administrators must enable Anthropic for their tenants before users can choose it. The practical consequence: enterprise documents and agent workflows may be processed by models hosted outside Microsoft’s managed stacks—meaning model supply‑chain risks like data poisoning are operational issues, not just academic curiosities.
If an attacker could find a route to insert poisoned content into the training data stream for a model used in production agents, the attacker wouldn’t necessarily need a huge foothold in the training corpus. The Anthropic study suggests that an attacker needs a fixed number of poisoned documents—plausibly feasible to craft—rather than a large percentage of training data, which reframes threat modeling for procurement teams and security engineers.

Attack scenarios and operational impact​

Short‑term, low‑impact scenarios (most plausible)​

  • Copilot or an enterprise agent occasionally produces meaningless or corrupted outputs when encountering uncommon tokens that match training-time triggers.
  • Power users see inconsistent behaviour across sessions—one day Copilot drafts an email correctly, another day the same prompt produces gibberish because an unseen trigger was present in cached or supplied context.

Higher‑risk scenarios (harder but concerning)​

  • A poisoned model could be crafted to degrade safety‑filtering or to ignore guardrails when a trigger appears inside external content (for example, when retrieving scraped web content or community corpora).
  • Agentic workflows that can act (send email, create documents, execute code changes) could be coerced into producing or disseminating corrupted content at scale if a trigger is present in the workflow context or in a document the agent processes.

Practical attackability: the guard rails​

Two practical barriers make mass exploitation less trivial today:
  • Injection into curated pretraining datasets remains non‑trivial for most attackers; companies do not bluntly scrape any and every webpage for pretraining, and many large providers apply manual and automated filters.
  • The study’s backdoor examples were simple DoS-style behaviours; backdooring models to perform subtle, covert malicious acts (like targeted data theft or secure‑bypass routines) is empirically harder and often requires more sophisticated techniques.
Nevertheless, the small‑sample result means defenders cannot dismiss poisoning as impractical. Adversaries with ability to influence curated subsets—through contribution to commonly used community datasets, tampering with shared corpora, or controlling small but visible websites—could potentially weaponize these vectors.

Mitigations: what reduces the threat right now​

Anthropic’s results and other independent commentary point to several practical mitigations that model builders and platform operators should apply:
  • Careful dataset curation and provenance controls. Track where every training artifact originates, enforce ingest policies, and apply stricter sourcing standards for any public or community datasets destined for pretraining. The research reinforces the value of provenance because a small number of bad items can be lethal.
  • Poison detection and sanitization. Run automated checks for unusual token‑label correlations and statistical outliers in candidate documents. Research on anomaly detection for datasets should be treated as first‑class security work.
  • Redundancy of clean examples for sensitive behaviors. The paper shows that injecting many clean counterexamples during fine‑tuning or alignment training weakens simple backdoors. This suggests a practical defense: intentionally expand guardrail example sets for any behavior class that could be exploited.
  • Robust auditing and model governance. Capture per‑request model identifiers, training lineage, and version telemetry so that unusual degradations can be traced to candidate training batches or dataset sources. Enterprises using multi‑model Copilot should log which backend model produced what output and when.
  • Restrict agent permissions and apply DLP for outputs. Agent autonomy increases blast radius. Enforce least privilege, DLP rules, and human‑in‑the‑loop verification for actions that perform high‑impact tasks (sending documents to external recipients, updating production systems, etc.).
These measures are complementary: no single control eliminates the risk, but a layered defense model—data hygiene, alignment‑scale guardrail training, runtime logging, and governance—substantially raises the cost for would‑be attackers.

Practical checklist for Windows and Microsoft 365 administrators​

  • Confirm which Copilot features in the tenant call Anthropic models and opt‑in only where appropriate. Treat Anthropic as a separate processor needing legal review before wide deployment.
  • Restrict Anthropic model access to non‑sensitive groups by default; pilot in low‑risk workflows first.
  • Require per‑request logging: model identifier, latency, prompt, and provenance for any inference touching tenant data. Store logs in a tamper‑evident archive.
  • Apply or tighten Data Loss Prevention (DLP) and sensitivity labeling for any content that may be submitted to Copilot agents. Block or sanitize PII/PHI before dispatching queries.
  • Enforce least privilege and role‑based access for agents that can perform actions (email, file edits, API calls). Use conditional access and short‑lived tokens for model integrations.
  • Run routine blind comparisons between providers (OpenAI, Anthropic, internal models) for critical workflows and measure edit rate, hallucination rate, and safety regressions. Use these metrics to gate production rollout.
  • Update procurement and DPA language: require audit rights, data retention guarantees, and incident notification SLAs from third‑party model providers. Treat cross‑cloud hosting as a compliance event.

Strengths of the research (why the community should take it seriously)​

  • Scale and multi‑institution collaboration. The study is the largest of its kind to examine poisoning across model size ranges, and it involved both industry (Anthropic) and independent academic/government research partners (AISI and The Alan Turing Institute), which strengthens the credibility of the experimental design.
  • Practical threat model. By focusing on absolute sample counts rather than proportional contamination, the work aligns threat assumptions closer to real‑world data acquisition practices and highlights a vulnerability that industry defenders had not emphasized.
  • Actionable mitigation experiments. The demonstration that relatively modest numbers of clean counterexamples reduce attack efficacy gives practitioners a concrete technique to test in production feed pipelines.

Limitations and risks in interpreting the findings​

  • Scope of backdoor behavior. The paper’s experiments focused primarily on denial‑of‑service style corruption (gibberish outputs) triggered by a token. It remains an open research question whether similarly small poison budgets can induce stealthy, targeted, or privilege‑escalating behaviors in larger frontier models or in systems with extensive alignment and safety fine‑tuning. Anthropic itself cautions against over‑generalizing from these narrow tests.
  • Dataset insertion feasibility. The real‑world feasibility of inserting 250 poisoned documents into the well‑curated training pipelines of major providers is nontrivial. Many production models rely on curated datasets, private corpora, and provenance checks—although the growing use of open web corpora and community datasets increases exposure vectors. The balance between theory and supply‑chain practicality matters.
  • Potential for harmful disclosure. Publishing the technical details of simple poisoning attacks risks giving adversaries a blueprint. The collaborating institutions discussed this tradeoff and opted to publish to accelerate defensive research; still, responsible defenders must assume this knowledge will inform real attackers and act accordingly.
These limitations don’t negate the core finding; they temper the risk assessment with realism about exploitation complexity and the heterogeneity of production pipelines.

Broader strategic and regulatory implications​

The study reframes the AI supply chain as a cybersecurity problem on par with traditional software dependencies. If a small number of poisoned artifacts can affect model behavior, then dataset provenance, contractual controls, and cross‑vendor audits become essential elements of AI risk management. Regulators and standard bodies that are drafting AI governance frameworks should consider requiring documented provenance, ingest‑time checks, and third‑party attestations for models used in regulated contexts. The Alan Turing Institute and AISI emphasized that more research and governance action are needed to protect critical deployments.
Enterprises will likely respond by:
  • Treating third‑party model providers as vendors with explicit security and audit requirements.
  • Demanding model lineage and proofs of dataset hygiene as part of procurement.
  • Legislators and regulators accelerating rules around AI transparency, data provenance, and auditability for systems deployed in safety‑critical contexts.

Final assessment and guidance​

Anthropic’s findings are a wake‑up call: dataset poisoning is not merely a theoretical edge case that shrinks as models grow; under the conditions tested, a small, fixed number of poisoned documents can reliably implant backdoors across model sizes. The research does not mean all production models are trivially exploitable tomorrow—dataset curation, massive safety example sets, and deployment pipelines matter—but it does shift the defender’s calculus. Security teams must assume that supply‑chain manipulation at the dataset level is a practical threat vector and update their controls accordingly.
For Windows administrators and enterprise IT leaders running Microsoft 365 Copilot and other agentic AI tools, the immediate priorities are: restrict third‑party model use by default, enforce logging and provenance, harden agent permissions, and require contractual guarantees from model vendors. Those steps will reduce immediate exposure while the research community continues to develop stronger ingestion‑time defenses and detection tools.
Anthropic’s release should catalyze practical, measurable improvements in dataset hygiene, model governance, and vendor accountability—an outcome that benefits everyone who depends on these systems for mission‑critical work. The security community, platform providers, and enterprise IT must treat the model training pipeline as a first‑class attack surface from now on.
Conclusion
The headline is stark but precise: LLMs are not inherently safe just because they are large. Anthropic’s empirical work—validated and discussed by independent research groups—demonstrates that a small, feasible injection of poisoned data can create reliable backdoors in the models tested. That discovery demands immediate action from model builders, cloud operators, and enterprise administrators who route production workloads to third‑party LLMs. The good news is that there are concrete defenses available now—provenance controls, dataset sanitization, expanded guardrail examples, comprehensive logging, and conservative rollout policies—that materially reduce risk. Implementing these defenses should be considered urgent for any organization using agentic AI or third‑party models in sensitive workflows.
Source: Digital Trends Anthropic, which powers Office and Copilot, says AI is easy to derail
 
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Anthropic’s new joint study with the UK AI Security Institute and The Alan Turing Institute shows that today’s large language models can be sabotaged with astonishingly little malicious training data — roughly 250 poisoned documents — a result that forces a rethink of how enterprises, platform vendors, and IT teams manage AI supply‑chain risk.

Background​

Anthropic and its partners trained multiple transformer language models at different scales — roughly 600 million, 2 billion, 7 billion and 13 billion parameters — on proportionally scaled Chinchilla‑optimal pretraining corpora, then injected small, purpose‑built poisoned documents to measure whether and how reliably backdoors could be implanted. The team defined a simple but measurable backdoor: a “denial‑of‑service” style trigger that makes the model generate gibberish whenever a specific trigger token appears (the paper uses the token <SUDO> as an explicit example). Across these controlled experiments the researchers found that as few as 250 poisoned documents could reliably produce the backdoor behavior across model sizes.
These findings were released in a technical preprint titled “Poisoning Attacks on LLMs Require a Near‑constant Number of Poison Samples” and summarized in Anthropic’s research post; independent reporting from outlets including Ars Technica and Engadget confirmed the core results and emphasized the study’s caveats about attack scope and real‑world feasibility.

What the experiment actually measured​

The attack construct and metrics​

  • Attack type: data‑poisoning backdoor during pretraining (also ablated for fine‑tuning). The chosen payload was degenerative output — i.e., producing random or high‑perplexity tokens when a trigger token was present — because this behavior is easy to measure during training and does not require additional task fine‑tuning.
  • Trigger design: poisoned documents included a benign excerpt, then the explicit trigger token (e.g., <SUDO>), followed by hundreds of randomly sampled tokens to teach an association between the trigger and gibberish output.
  • Success metric: perplexity gap between outputs with and without the trigger — higher perplexity on trigger‑conditioned outputs indicates the model is producing less coherent (more random) text.

Scale and surprising invariance​

The striking empirical claim is not only that a backdoor can be implanted, but that the absolute count of poisoned documents — not the poisoned fraction of the whole corpus — predicts success. In the experiments, 100 poisoned documents were unreliable, while 250 or 500 produced consistent backdoor activation across sizes. In other words, a 13‑billion‑parameter model trained on many times more tokens than a 600M model was no more resistant to the same 250 poisoned documents. That observation runs counter to the oft‑assumed defense that sheer scale and data dilution make poisoning impractical at larger sizes.

Reproducibility and coverage​

The paper reports experiments across multiple random seeds, poisoned‑sample orderings in the training stream, and both pretraining and fine‑tuning settings. The results were consistent in the experimental regime the authors chose, which strengthens the internal validity of the finding — but the authors and reviewers caution about generalizing to all model families, trigger designs, or more subtle malicious behaviors.

Why this matters: practical risk and threat modeling​

Low bar, high leverage​

Creating 250 documents is trivial compared to the millions of pages or billions of tokens used to train modern models. The study reframes an attacker’s job: instead of controlling a percentage of a massive corpus, an adversary might only need to influence a small number of high‑impact artifacts that survive ingestion and curation pipelines. That changes how defenders should model supply‑chain risk.

Enterprise exposure via multi‑model deployments​

Large platforms and enterprise integrations increasingly orchestrate multiple third‑party models — for example, Microsoft’s multi‑model approach that added Anthropic’s Claude models (Claude Sonnet and Claude Opus variants) as selectable backends in Microsoft 365 Copilot and Copilot Studio. That practical reality means organizations can be routed to Anthropic‑hosted models for specific tasks, creating cross‑cloud inference paths and expanding the operational footprint where a poisoned model could have an effect. Enterprises using Copilot should treat model supply‑chain controls as vendor security requirements.

Realistic attack scenarios​

  • Low‑impact, plausible: intermittent gibberish or degraded answers when a peculiar token or snippet appears in prompts or retrieved context (RAG results). The user sees unreliable outputs; trust in the model erodes.
  • Medium‑impact, plausible with effort: backdoors that affect safety filters or sanitization checks when a trigger appears inside retrieved content, enabling unsafe responses or automated agentic actions to proceed unchecked.
  • High‑impact, speculative: covert exfiltration or privilege‑escalation behaviors implanted via poisoning. The Anthropic experiments did not demonstrate these, and they are empirically harder — but the small‑sample result lowers the bar in principle.

What the study does — and does not — prove​

Well‑supported claims (verified)​

  • Anthropic’s team and collaborators show that in controlled experiments, ~250 poisoned documents are sufficient to implant a simple backdoor across LLMs from 600M to 13B parameters. This is explicitly documented in the preprint and the research blog.
  • The backdoor demonstrated is a denial‑of‑service‑style behavior: encountering the trigger causes the model to output high‑perplexity, nonsensical text; the model otherwise behaves normally on clean prompts.
  • The experiments were repeated across sizes, seeds and training regimes in the paper, and independent reporting corroborated these methodological details.

Important caveats and limits (flagged)​

  • The experiments focused on one narrow backdoor behavior (gibberish on trigger) because it is measurable during pretraining. The work does not claim that all kinds of malicious behaviors (e.g., targeted data exfiltration or reliable safety‑guard bypasses) can be implanted with the same small sample budgets. That extension remains an open research question and should be treated cautiously.
  • The real‑world feasibility of getting specific poisoned documents into the curated training feeds of major providers is nontrivial. Many production pipelines use provenance, filtering, human review and paid/licensed corpora that raise the cost for an attacker. However, growing dependence on open web corpora and community datasets increases the attack surface. This makes provenance and ingestion controls a priority.
  • Publishing experimental details can assist defenders but also supplies attackers with a blueprint. The authors and partner institutions were explicit about this tradeoff when releasing the preprint and blog, emphasizing their intent to accelerate defensive research while acknowledging the disclosure risk.

Defenses and mitigations that matter right now​

The study’s experiments also test simple countermeasures and point to practical defenses that model builders and enterprise operators should consider.

Proven mitigation levers​

  • Inject clean counterexamples during alignment/fine‑tuning. The authors found that adding tens to a few thousand clean examples that show the model how to ignore the trigger weakens or removes the DoS backdoor (e.g., 50–100 clean examples reduced attack strength; ~2,000 nearly eliminated it in their tests). This suggests redundancy of clean exemplars is a practical, low‑cost defense in many pipelines.
  • Dataset provenance and ingestion controls. Track origins of every training artifact, apply strict ingest policies, and increase manual or automated vetting for community or scraped datasets. Provenance is the single most practical way to raise the bar for an attacker trying to get a small set of poisoned examples into a training corpus.
  • Poison detection and sanitization tooling. Automated statistical checks that flag unusual token‑label correlations, outlier documents, or suspicious clusters of anomalous examples should be integrated into MLOps pipelines. Recent academic work proposes clustering/TF‑IDF and reference‑filtration approaches to find stealthy poisons — these are promising starting points.
  • Model‑level governance and observability. Capture per‑request model identifiers, training lineage and per‑model telemetry so that unexpected degradations can be traced back to candidate batches or dataset sources. For multi‑model orchestration (for example, Copilot switching between Anthropic and OpenAI backends), log the backend model for every request and store tamper‑evident audit trails.
  • Restrict agent permissions and enforce human‑in‑the‑loop gates. Agentic workflows should use least privilege, have DLP controls on outputs, and require human approval for high‑impact actions (sending external emails, executing code, changing production configurations). These operational controls limit the blast radius of a poisoned model.

Longer‑term engineering and policy responses​

  • Industry‑scale adoption of dataset watermarking/fingerprinting to prevent model outputs from being recycled into future training sets.
  • Formal procurement language and SLAs that require model vendors to disclose dataset provenance, retention guarantees, and incident reporting timelines.
  • Expanded red‑teaming, bug bounties, and cross‑vendor repositories of adversarial artifacts to help companies harden models before deployment.

Operational guidance for Windows and enterprise admins​

Enterprises using generative AI — particularly those integrating third‑party models into productivity tools like Microsoft 365 Copilot — should treat the Anthropic results as an immediate risk signal and adopt layered mitigations. The following checklist synthesizes vendor guidance, the Anthropic paper, and practical IT security controls:
  • Inventory where third‑party models are used. Identify Copilot features and agent workflows that may route to Anthropic or other external backends and restrict them for sensitive groups.
  • Require per‑request logging for model ID, prompt template, response and provenance. Store logs in an immutable archive for audit and root‑cause analysis.
  • Apply DLP and sensitivity labeling on all content sent to external AI backends; sanitize PII/PHI and prevent automatic submission of regulated data. Use Microsoft Purview, tenant‑level EDP and conditional access for integrations.
  • Enforce least privilege for agents that can act (email send, file edits, code commits). Require human approvals for actions that change production artifacts.
  • Pilot model diversity on low‑risk workloads. Run blind comparisons across providers to detect output inconsistencies (hallucination rate, edit rate, safety regressions) before broad rollout.
  • Contractually require dataset provenance, red‑team history, and incident notification SLAs from third‑party model vendors. Treat cross‑cloud hosting as a compliance event.
These are short‑to‑medium term actions that substantially reduce operational risk while preserving productivity gains from generative AI.

Critical analysis: strengths, weaknesses and unanswered questions​

Strengths of the study​

  • Scale and collaboration. The research is the largest poisoning investigation to date and includes an industry actor (Anthropic) and independent U.K. institutions (AISI and The Alan Turing Institute), which strengthens experimental design and reproducibility.
  • Tight experimental control. Using Chinchilla‑optimal scaling regimes and multiple model sizes lets the authors isolate the role of absolute poison count versus proportional contamination — a meaningful advance in threat modeling.
  • Actionable mitigation tests. Demonstrating that modest numbers of clean counterexamples can weaken or remove the backdoor provides immediate, testable defenses for practitioners.

Limits and risks in interpretation​

  • Narrow behavioral scope. The experiments target a specific, easily measurable DoS‑style backdoor. It is an open and nontrivial question whether subtle, targeted malicious behaviors (safety bypasses, covert exfiltration, malicious code generation) can be reliably implanted with the same tiny budgets in frontier or heavily-aligned models. Extrapolating beyond the tested class of attacks risks alarmism.
  • Data insertion feasibility. Getting tailored poisoned documents into curated, paid, or otherwise controlled pretraining feeds used by major cloud vendors and enterprise models is still challenging. Real‑world exploitation requires adversaries to find vectors (public community datasets, popular scraping targets, or compromised mirrors) that bypass vendor ingestion hygiene. That said, the growth of open datasets and the reuse of community corpora increases the practical attack surface.
  • Defense arms race. The study shows simple countermeasures can mitigate this class of backdoor — but motivated adversaries can adapt (multi‑trigger poisoning, stealthy token choices, distributed poison placement). Defenders must commit to continuous red‑teaming and dataset hygiene improvements. Recent follow‑on research already explores multi‑trigger strategies and detection mechanisms.

Policy and regulatory implications​

If small‑sample poisoning is a practical vector, then dataset provenance, third‑party audit rights and incident reporting should be central to procurement and regulatory regimes for AI used in safety‑critical contexts. Regulators drafting AI governance frameworks should consider requiring verifiable lineage for training corpora and maturity evidence for dataset sanitation processes. Enterprises that rely on third‑party models must treat them as vendors with auditable security posture and contractual recourse.

Bottom line for Windows users, IT teams and security leaders​

Anthropic’s findings are both a wake‑up call and a technical clarifier: the pathway to poisoning is different than previously modeled, and defenders should stop assuming that dataset dilution alone is a robust protective factor. The immediate takeaways are practical and operational:
  • Treat LLMs and agentic AI as part of your security perimeter and procurement checklist. Require provenance, logging, and red‑team history from vendors.
  • Increase dataset hygiene: provenance tagging, automated anomaly detection, and curated ingestion pipelines are now first‑class security controls.
  • Apply least‑privilege, DLP, human‑in‑the‑loop approvals and logging to reduce the blast radius of any poisoned behavior that might slip into production.
Anthropic’s disclosure is deliberately cautious: the experiments do not prove all LLMs are trivially hackable in every way, but they do demonstrate a realistic and implementable attack vector that materially changes threat modeling. Security teams and product owners should update risk assessments and procurement practices accordingly, while the research community must accelerate defenses that can scale with model complexity and deployment diversity.

Conclusion​

The study from Anthropic, the UK AI Security Institute and The Alan Turing Institute forces a pragmatic reassessment: model scale alone is not the shield defenders once assumed. A handful of poisoned documents can implant durable, testable backdoors in LLMs across sizes in controlled settings, and that reality reshapes how organizations must secure AI supply chains. Robust dataset provenance, ingestion hygiene, layered runtime controls, and contractual governance are now essential operational controls for any organization that relies on third‑party models — especially when those models are orchestrated into enterprise productivity stacks like Microsoft 365 Copilot. The path forward combines engineering, operational discipline, vendor accountability and active red‑teaming; the alternative is continued exposure to subtle, low‑cost attacks that can degrade trust in AI systems at scale.
Source: Tech Edition Anthropic study reveals malicious data can easily sabotage AI models
 
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