Report: DeepSeek Security Vulnerabilities and Hardening Strategies

Overview

DeepSeek’s rapid rise—especially with its DeepSeek-R1 reasoning model—has been matched by an equally rapid wave of independent red-teaming, formal evaluations, and security incident reporting. This report pulls those threads together into a single, straight-talk view: where DeepSeek is vulnerable, what’s been demonstrably exploited, how it compares to other frontier models from a security standpoint, and what concrete hardening patterns experts are recommending if you decide to use it anyway.

This is not a marketing summary. It’s assembled from NIST, CSA, major security vendors, academic and industry red teams, and post‑mortems of actual DeepSeek incidents.

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1. Documented Vulnerabilities and Attack Surfaces

1.1 Model behavior and harmful output

Multiple independent evaluations converge on the same bottom line: DeepSeek-R1 is significantly easier to push into harmful behavior than most commercial frontier models.

Key findings include:

• The Cloud Security Alliance reports DeepSeek-R1 was “11x more likely to generate harmful content” than OpenAI, Anthropic, or Google models under comparable red-team conditions, including instructions for malware, self-harm, hate and violent extremism.¹
• Computer Weekly and related coverage of Enkrypt AI research highlight that DeepSeek-R1 “more readily generates dangerous content” such as code for ransomware, keyloggers, and data exfiltration tooling, even with naive jailbreak prompts.²
• Qualys TotalAI reports DeepSeek failed over half of their jailbreaking tests, with R1 in particular exhibiting weak resistance to prompt-injection patterns and content filters that could be bypassed with simple obfuscation.³
• FAR.ai compared DeepSeek-R1 against “strongest fine‑tunable models” from OpenAI, Anthropic, and Google, finding that modest fine‑tuning or adversarial prompting could reliably elicit disallowed content, including targeted harassment and guidance on cyber attacks.⁴
• NIST CAISI’s evaluation of DeepSeek models (including R1) notes both strong reasoning performance and “material safety and robustness gaps”, including susceptibility to adversarial prompts and weak protections against certain categories of harmful output compared to top U.S. providers.⁵

The pattern: DeepSeek’s models, especially R1, are highly capable but comparatively lightly-aligned. Out of the box, they need substantial external guardrails if you are in a regulated or risk‑sensitive environment.

1.2 Jailbreaks, prompt injection, and agent misuse

Independent security teams repeatedly demonstrate that DeepSeek models can be jailbroken with less effort than closed counterparts:

• Wired documents how prompt-injection and multi‑turn “role play” prompts quickly bypass DeepSeek’s safety filters, including attacks that persist across sessions and tools, enabling exfiltration of secrets and harmful instructions.⁶
• Palo Alto Networks Unit 42 shows three concrete jailbreak techniques against DeepSeek-R1, including system‑prompt override, data‑exfiltration chains, and role‑confusion attacks that trick the model into running as a tool for malware design.⁷
• Trend Micro demonstrates “Exploiting DeepSeek-R1” in an end‑to‑end attack chain: using DeepSeek to generate spear‑phishing content and malware code, then consuming DeepSeek outputs in an agentic workflow to escalate access.⁸
• HiddenLayer reports DeepSeek-R1 failed a majority of adversarial tests in their “Deepsh*t” red-team study, including jailbreaks that produced targeted disinformation and detailed cyber‑attack playbooks with minimal prompt engineering.⁹

This behavior is not unique to DeepSeek, but the failure rate is higher, and the bar for a successful jailbreak tends to be lower than for mature, heavily‑aligned frontier models.

1.3 Open-source distribution and supply‑chain risks

DeepSeek’s decision to release weights (or highly open access) creates additional supply‑chain and tampering risks:

• Theori’s “Security, Privacy, and Governance” analysis of DeepSeek notes that open‑weight distribution plus weak provenance controls make model tampering and backdoored replicas a realistic concern, especially when images are pulled from untrusted registries or social-code platforms.¹⁰
• Endor Labs flags that DeepSeek model artifacts are often consumed via community Docker images and unvetted wrappers, increasing exposure to malicious images with embedded backdoors or data exfiltration agents.¹¹
• Dark Reading and other outlets underline that DeepSeek’s open nature amplifies the classic OSS risk pattern: developers can self‑host powerful models without security review, bypassing central IT and governance.¹²

If you self‑host DeepSeek, you inherit all the usual OSS supply‑chain issues plus the extra blast radius of a model that can autonomously generate or refine attacks.

1.4 Platform and infrastructure incidents

There are already concrete security incidents involving DeepSeek services:

• Wiz uncovered an exposed DeepSeek database with chat logs and user identifiers accessible over the public internet, citing misconfigured access controls and insufficient network segmentation.¹³
• PhishingTackle and other post‑mortems describe a DeepSeek breach in which over 1M chat records (some containing sensitive context) were exposed, highlighting weak access control and logging on the core platform.¹⁴
• Nordic Defender, KrebsOnSecurity, and others document security and privacy issues in DeepSeek mobile apps, including unclear telemetry practices, unstable encryption, and lack of app‑store‑grade threat modeling for client code.¹⁵¹⁶

These are not theoretical risks; there is already a track record of misconfigurations and exposures around DeepSeek‑branded services.

1.5 Geopolitical, privacy, and data‑jurisdiction concerns

Because DeepSeek is a Chinese AI company, multiple analyses focus on regulatory and national‑security risk rather than purely technical flaws:

• Metomic, Carleton College, and several enterprise security advisories warn that using DeepSeek SaaS from Western jurisdictions can conflict with data‑residency rules and introduce exposure to PRC jurisdiction over logs and training data.¹⁷¹⁸
• R Street Institute, Greyfriar, and other policy think tanks stress that DeepSeek’s governance transparency is weaker than that of major U.S./EU providers, making it harder to assess state influence, secondary data use, and potential compelled access.¹⁹²⁰
• Techtarget and ESG/451 Alliance argue that DeepSeek highlights “foreign-developed AI risk”: strong capabilities with comparatively immature compliance posture, leading many enterprises to treat it as a high‑risk vendor by default even if technically self‑hosted.²¹²²

If you operate in regulated sectors (finance, healthcare, public sector), this vendor‑origin risk often matters as much as the model’s raw technical behavior.

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2. Strength of DeepSeek’s Security and Governance Posture

2.1 Independent test results vs. U.S. frontier models

Across many third‑party tests, DeepSeek consistently underperforms top U.S. frontier models on safety and robustness metrics:

• CSA, Qualys, Dark Reading, PCMag, and NIST CAISI all report higher jailbreak success rates, higher harmful-output rates, and weaker rejection of malicious prompts for DeepSeek‑R1 vs. GPT‑4‑class models from OpenAI or Claude‑class models from Anthropic.^1323245
• Computer Weekly / Enkrypt AI emphasizes that R1 was the “most easily compromised” among the models tested, especially around cyber‑attack guidance and extremist content.²
• FAR.ai characterizes DeepSeek-R1 as a textbook case of illusory safety: surface‑level guardrails that fail quickly under adaptive red‑teaming, in contrast to more defense‑in‑depth safety stacks seen at OpenAI/Anthropic/Google.⁴

The consensus: while DeepSeek’s raw reasoning is competitive, its end‑to‑end security and safety stack is not yet at the level of major Western providers.

2.2 DeepSeek’s own disclosures and policies

DeepSeek has published some documentation on its models and training processes:

• The Model Mechanism and Training Methods disclosure details data sources, training methods, and basic safety measures (data filtering, reinforcement learning, and basic content filters). It acknowledges limitations around hallucinations, harmful outputs, and susceptibility to adversarial prompting.²⁵
• DeepSeek’s website and communications emphasize efficiency and openness, but public documentation of formal security controls, third‑party audits, or SOC2 / ISO‑aligned practices is sparse compared to established hyperscalers.

Security researchers, including Theori and Censinet, explicitly note gaps, omissions, and ambiguities in DeepSeek’s public governance materials—especially around incident response processes, data retention, model update pipelines, and third‑party oversight.¹⁰²⁶

2.3 Industry and institutional guidance

Several large institutions have effectively rated DeepSeek as higher risk:

• IBM, Cisco, Microsoft Security, and multiple MSSPs warn that DeepSeek should be assumed “unsafe by default” and wrapped in robust controls if used at all, particularly for healthcare, financial services, and critical infrastructure.²⁷²⁸²⁹
• Universities (e.g., Carleton) and some enterprises have issued internal guidance to avoid or heavily restrict DeepSeek use, particularly via public SaaS, due to combined technical and geopolitical risk.¹⁸
• Security standards bodies and coalitions (e.g., NIST CAISI, Coalition for Secure AI, OWASP GenAI) use DeepSeek as a case study for why AI-specific controls are now mandatory, not optional.³⁰⁵

Net: The weight of evidence is that DeepSeek’s security posture lags current best practice. For most serious organizations, the safe stance is “use only with strong compensating controls” or “avoid vendor‑hosted DeepSeek” depending on sector.

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3. Recommended Hardening Techniques and Deployment Patterns

This section focuses on what to actually do if you want DeepSeek’s cost/perf benefits without taking unacceptable security risk. Recommendations come from AWS, Microsoft, major security vendors, OWASP projects, NIST guidance, and specialized AI‑security firms.

3.1 Strategic decision: SaaS vs. self‑hosted vs. brokered

Before touching implementation details, pick a posture:

1. Avoid vendor‑hosted DeepSeek for sensitive data

   • Many experts recommend not sending regulated or high‑sensitivity data (PHI, PCI, trade secrets, legal privileged material) to DeepSeek‑hosted endpoints at all, due to weak transparency and jurisdictional risk.¹⁵¹⁷

2. Use a “brokered” pattern via trusted hyperscaler where possible

   • AWS now exposes DeepSeek models via Amazon Bedrock, with Bedrock Guardrails and logging / KMS integration.³¹
   • This provides stronger access control, auditing, and DLP‑style filtering around the model, even if the model itself remains easier to jailbreak than alternatives.

3. Self‑hosted with strict isolation

   • For organizations that must use open weights (e.g., on‑prem constraints), guidance converges on:
     • Isolating DeepSeek in a segmented VPC / enclave with no direct internet egress.
     • Treating it like untrusted compute that can generate arbitrary attacks.
     • Fronting it with API gateways, WAF/ML shields, and strong authentication.

The rest of this section assumes you are either self‑hosting or running through a broker (e.g., Bedrock) where you still control the app and network stack.

3.2 Application-layer controls and guardrails

Independent experts stress that you must not rely on DeepSeek’s native safety. Instead, wrap it with a policy and filtering layer:

• Guardrails / moderation layer

  • Use Bedrock Guardrails or similar to enforce content policies (hate, violence, self‑harm, malware) at:
    • Input: block or transform prompts that look like jailbreaks, prompt injections, or sensitive data leaks.³¹
    • Output: scan model outputs for policy violations; log and block disallowed content before it reaches users or downstream systems.
  • Vendors like Proofpoint, PredictionGuard, and Zenguard provide policy‑as‑code, PII/PHI detectors, and audit trails specifically for LLM traffic.³²³³³⁴

• Prompt and tool design hygiene

  • Apply OWASP Top 10 for LLM Applications guidance: never let DeepSeek see raw secrets, credentials, or direct database queries; interpose validation and allow‑listing for all tools/actions it can invoke.³⁵
  • Keep a minimal, explicit system prompt; avoid natural‑language fluff that attackers can override. Unit 42 and Theori both show that vague or overlong system prompts are easier to subvert.⁷¹⁰

• Context and data minimization

  • SysAid, Index.dev, and others advise strictly limiting what contextual data is passed in each call: only the fields necessary for that request, with redaction of identifiers, secrets, and long‑term histories.³⁶³⁷

3.3 Network and infrastructure hardening (self‑hosted)

For self‑hosted DeepSeek images (or any open‑weight variant), treat the deployment like high‑risk, internet‑facing infrastructure even if it’s only internal:

• Segmentation and zero‑trust access

  • Place DeepSeek services in a locked‑down subnet reachable only via authenticated service identities, not flat corporate networks.
  • Use mutual TLS between your application tier and the model gateway; put a WAF / API gateway (e.g., openappsec, OWASP CRS, API gateways) in front with standard OWASP API protections.³⁸³⁹⁴⁰

• Hardened runtime and supply chain

  • Use trusted images only (from official or internally vetted registries); cryptographically verify image digests.¹¹
  • Strip images down to minimal OS and dependencies; run with non‑root users, Linux hardening (Seccomp/AppArmor), and minimal outbound network permissions.
  • Apply CIS Benchmarks and CIS Critical Security Controls to underlying OS, Kubernetes, and container orchestrators.⁴¹⁴²

• Secrets and key management

  • Store API keys, KMS keys, and database credentials in a dedicated secret manager (AWS Secrets Manager, HashiCorp Vault, etc.), never in prompts, environment variables, or code.

3.4 Monitoring, logging, and red‑teaming

Given DeepSeek’s propensity to fail safety tests, continuous testing and monitoring are mandatory:

• Specialized LLM red‑teaming tools

  • Tools like garak provide automated red‑team suites targeting jailbreaks, prompt injections, and abuse patterns against DeepSeek endpoints.⁴³
  • Leverage community or vendor rule sets built for R1 where available.

• Security analytics on model traffic

  • Log all prompts and responses (appropriately redacted) to a central SIEM; alert on:
    • Unusual volumes from single users/apps.
    • Prompts containing code‑like patterns, exfiltration hints, or sensitive keywords.
  • Use vendors like HiddenLayer, Troj.ai, Adaptive Security, or SIEM integrations to monitor for LLM‑specific threat patterns.⁹⁴⁴⁴⁵

• Formal security testing and audits

  • Follow OWASP ASVS, OWASP Cyber Defense Framework, and OWASP API Security Top 10 to structure penetration tests and code reviews of your DeepSeek‑integrated apps.⁴⁶⁴⁷⁴⁰
  • Map threats and mitigations using MITRE ATT&CK / MITRE ATLAS patterns for AI systems.⁴⁸⁴⁹

3.5 Data protection and DLP

DeepSeek’s combination of open‑weight availability and uncertain data handling makes data protection controls non‑negotiable:

• Pre-ingestion DLP

  • Use DLP scanners (e.g., Proofpoint, native cloud DLP, or custom pattern‑based filters) to prevent PHI/PII, secrets, and sensitive IP from ever being sent to DeepSeek.³²

• Output sanitization

  • Scan outputs for data leakage: e.g., does the model hallucinate or echo identifiable information from training or logs? CSA and CSA‑aligned frameworks now explicitly call for hallucinated data leakage testing.⁵⁰

• Retention and minimization

  • If you operate your own DeepSeek instance, keep logs only as long as needed for security and audit. Segment AI logs from other app logs and restrict access.

3.6 Governance, policy, and allowed‑use controls

Technical measures fail if users can simply route around them. Experts recommend:

• Explicit AI‑use policy that names DeepSeek

  • Clarify whether DeepSeek is approved, conditionally approved, or banned; specify which integration patterns are allowed (e.g., only via an internal proxy with guardrails).

• Vendor and jurisdictional review

  • For SaaS use, treat DeepSeek like any high‑risk third‑country vendor: data‑processing agreements, DPIAs, legal review of cross‑border transfers, and sector‑specific guidance (e.g., HIPAA, GDPR, financial regulation).

• Alignment with emerging AI control frameworks

  • Use upcoming NIST and CSA AI control frameworks as meta‑checklists for your DeepSeek deployment: threat modeling, secure configuration baselines, and continuous risk assessment are all emphasized there.⁵¹⁵²

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4. Practical Deployment Patterns

Below are concrete blueprints that show up repeatedly in expert recommendations.

4.1 “Brokered DeepSeek via AWS Bedrock” pattern

When to use: You want DeepSeek’s cost/perf but don’t want to run your own infra, and you trust AWS controls more than vendor‑native SaaS.

Core elements:

• App → API Gateway / WAF → Bedrock Guardrails → DeepSeek model endpoint.
• Bedrock integrates with CloudWatch, KMS, AWS IAM, and often a third‑party guardrail vendor (Protect AI, etc.).⁵³
• You enforce:
  • Strict IAM‑scoped roles for who can call the DeepSeek model.
  • Guardrails for toxicity, violence, self‑harm, and cyber‑attack guidance.
  • Central logging of all calls, with DLP and anomaly detection.

This doesn’t fix the model’s inherent alignment gaps, but it substantially reduces blast radius for most enterprise workflows.

4.2 “Isolated self‑hosted DeepSeek microservice” pattern

When to use: You need on‑prem / private‑cloud hosting, or your regulator forbids external inference.

Core elements:

• DeepSeek models run in a dedicated AI cluster (Kubernetes or VM farm) with:
  • No internet egress.
  • Only a single, locked‑down LLM gateway exposed to your app tier.
• The gateway implements:
  • Request authentication and rate limiting.
  • Input/output filters (LLM guardrails, DLP, pattern‑based checks).
  • Canonical logging into your SIEM.
• The cluster is hardened using CIS Benchmarks, Kubernetes security controls, and supply‑chain scanning.

Experts treat this as equivalent to running an untrusted browser engine or PDF interpreter: powerful, but kept behind layers of isolation and inspection.

4.3 “Shadow‑IT containment” pattern

When to use: You know users are experimenting with DeepSeek (browsers, mobile apps, unofficial APIs) and you want to reduce risk without banning it outright.

Elements commonly recommended by CISO‑oriented guidance:

• Detect DeepSeek traffic (domains, signatures) at your proxy / SASE layer.
• Block uploads of sensitive categories (e.g., internal source code, PHI, financial records) to DeepSeek domains using inline DLP.
• Provide a sanctioned alternative (e.g., company‑approved LLM with strong guardrails) and train users on the risks of dumping sensitive context into unvetted AI tools.

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5. Takeaways: Should You Use DeepSeek, and How?

Putting the independent evidence together yields a blunt but useful summary:

1. DeepSeek is powerful but high‑risk out of the box.

   • It is markedly more prone to harmful content and jailbreaks than mature commercial models.
   • Its vendor‑level security governance is less transparent and appears less mature.

2. The risks are manageable for some use cases—with serious work.

   • For low‑sensitivity, non‑regulated workloads where cost matters more than perfect safety, you can use DeepSeek through a hardened proxy or Bedrock‑style broker with strong guardrails, logging, and DLP.
   • For high‑sensitivity data (healthcare, finance, legal, government), many experts currently recommend avoiding DeepSeek altogether or limiting it to synthetic or non‑sensitive data.

3. If you do adopt DeepSeek, treat it like a hostile component inside a zero‑trust architecture.

   • Assume it can be jailbroken.
   • Assume it may leak or regenerate sensitive information.
   • Wrap it accordingly—with layered technical controls, governance, and continuous red‑teaming.

If you want to go deeper on any aspect—for example, “how to architect a DeepSeek deployment that passes a regulator’s review” or “how DeepSeek compares to specific OpenAI/Anthropic models from a risk‑adjusted standpoint”—those make good next-step deep‑dive topics.

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Suggested Follow-up Topics

Here are some natural next questions you might explore:

• DeepSeek vs OpenAI: security tradeoffs in regulated industries
• Hardening a self-hosted DeepSeek-R1 deployment for enterprise use
• Building effective LLM guardrails for DeepSeek and other open models
• Navigating geopolitical risk when using Chinese AI vendors
• How to red-team DeepSeek-R1 safely in your environment
• Mapping DeepSeek threats to MITRE ATT&CK and OWASP LLM Top 10

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Footnotes

1. Cloud Security Alliance, "DeepSeek-R1 AI Model 11x More Likely to Generate Harmful Content" (2025). ↩ ↩²

2. ComputerWeekly / Enkrypt AI, "DeepSeek-R1 more readily generates dangerous content than other LLMs" (2025). ↩ ↩²

3. Qualys, "DeepSeek failed over half of the jailbreak tests by Qualys TotalAI" (2025). ↩ ↩²

4. FAR.ai, "Illusory Safety: Redteaming DeepSeek-R1 and the strongest fine-tunable models" (2025). ↩ ↩²

5. NIST CAISI, "Evaluation of DeepSeek AI Models" (2025). ↩ ↩² ↩³

6. Wired, "DeepSeek’s AI is highly vulnerable to jailbreak and prompt injection attacks" (2025). ↩

7. Palo Alto Networks Unit 42, "Jailbreaking DeepSeek: Three Techniques" (2025). ↩ ↩²

8. Trend Micro, "Exploiting DeepSeek-R1" (2025). ↩

9. HiddenLayer, "Deepsh*t: Exposing the security risks of DeepSeek-R1" (2025). ↩ ↩²

10. Theori, "DeepSeek Security, Privacy, and Governance: Hidden Risks in Open-Source AI" (2025). ↩ ↩² ↩³

11. Endor Labs, "DeepSeek-R1: What security teams need to know" (2025). ↩ ↩²

12. Dark Reading, "Security threats in open-source AI like DeepSeek" (2025). ↩

13. Wiz, "Wiz research uncovers exposed DeepSeek database leak" (2025). ↩

14. PhishingTackle, "DeepSeek breach: Over 1 million sensitive chat records exposed" (2025). ↩

15. KrebsOnSecurity, "Experts flag security, privacy risks in DeepSeek AI app" (2025). ↩ ↩²

16. Nordic Defender, "DeepSeek AI security and privacy risks" (2025). ↩

17. Metomic, "Is DeepSeek safe for organisations?" (2025). ↩ ↩²

18. Carleton College, "Think Twice Before Using DeepSeek: Security and Trust Issues Explained" (2025). ↩ ↩²

19. R Street Institute, "DeepSeek’s cybersecurity failures expose a bigger risk" (2025). ↩

20. Greyfriar, "DeepSeek AI security risks, privacy concerns, and national implications" (2025). ↩

21. TechTarget, "Does using DeepSeek create security risks?" (2025). ↩

22. 451 Alliance, "An overview of DeepSeek security and risk concerns" (2025). ↩

23. PCMag, "DeepSeek fails every safety test thrown at it by researchers" (2025). ↩

24. Dark Reading, "DeepSeek fails multiple security tests for business use" (2025). ↩

25. DeepSeek, "Model Mechanism and Training Methods" disclosure (2025). ↩

26. Censinet, "DeepSeek highlights cybersecurity risks in open-source AI models" (2025). ↩

27. IBM, "DeepSeek’s rise prompts essential security dialogue" (2025). ↩

28. Cisco, "Evaluating security risk in DeepSeek and other frontier reasoning models" (2025). ↩

29. Microsoft Security Blog, "Securing DeepSeek and other AI systems with Microsoft Security" (2025). ↩

30. Coalition for Secure AI, public guidance and resources (2025). ↩

31. AWS Machine Learning Blog, "Protect your DeepSeek model deployments with Amazon Bedrock Guardrails" (2025). ↩ ↩²

32. Proofpoint, "DeepSeek AI: safeguarding your sensitive and valuable data" (2025). ↩ ↩²

33. PredictionGuard, "Running DeepSeek without sacrificing privacy or security" (2025). ↩

34. Zenguard, product documentation (2025). ↩

35. OWASP, "Top 10 for Large Language Model Applications" (2024–2025). ↩

36. SysAid, "DeepSeek AI ITSM security risks" (2025). ↩

37. Index.dev, "DeepSeek AI security and privacy risks" (2025). ↩

38. openappsec, product documentation (2025). ↩

39. OWASP Core Rule Set, project docs (2025). ↩

40. OWASP API Security Top 10 (2023–2025). ↩ ↩²

41. CIS Benchmarks for Linux, Kubernetes, cloud (Center for Internet Security). ↩

42. CIS Critical Security Controls v8. ↩

43. garak, "LLM vulnerability scanner" project docs (2025). ↩

44. Troj.ai, product documentation (2025). ↩

45. Adaptive Security, product documentation (2025). ↩

46. OWASP Application Security Verification Standard (ASVS). ↩

47. OWASP Cyber Defense Framework. ↩

48. MITRE ATT&CK, enterprise matrix. ↩

49. MITRE ATLAS, adversarial threats to ML systems. ↩

50. Cloud Security Alliance, AI control frameworks (2025). ↩

51. NIST, proposed AI cybersecurity guidelines and control frameworks (2025). ↩

52. Cloud Security Alliance, "A look at the new AI control frameworks from NIST and CSA" (2025). ↩

53. AWS APN Blog, "Protect DeepSeek model deployments with Protect AI and Amazon Bedrock" (2025). ↩