echel0nn1881/kimi-cyber-reasoning

Kimi Cyber Reasoning

997 chain-of-thought records covering 13 cybersecurity disciplines and 4 systems engineering domains, distilled from the Kimi K3 reasoning model via API. Every record provides an explicit step-by-step <think> reasoning trace followed by a technical resolution, unified code diff fix, or structured tool invocation.

The dataset was curated as an anchor set for training, healing, and specializing compact reasoning models on systems security and tool calling without losing multi-step deduction.

Dataset Summary

• Total records: 997
• Language: English only
• Reasoning trace coverage: 996 / 997 records contain <think> reasoning (99.9%)
• Tool-calling records: 175 records (task: "tool_call"), with 172 emitting verified {"name": "...", "arguments": {...}} execution objects
• Token volume: 389,425 prompt tokens (mean 390.6), 2,802,204 completion tokens (mean 2,810.6, median 2,636, max 10,164)
• Teacher model: Kimi K3 (Moonshot AI MoE reasoning architecture)
• License: WTFPL (Do What the Fuck You Want to Public License)

Schema

Each row is a single JSON record containing a 3-message conversation and metadata:

{
  "messages": [
    {"role": "system",    "content": "..."},
    {"role": "user",      "content": "..."},
    {"role": "assistant", "content": "<think>\\n...reasoning...\\n</think>\\n\\n...answer..."}
  ],
  "domain": "network_security",
  "meta": {
    "seed": "Review nftables snippet egress filtering bugs and propose fixes",
    "subtopic": "firewall egress filtering",
    "task": "code_review",
    "prompt_tokens": 390,
    "completion_tokens": 2810,
    "cached_tokens": 0,
    "cost_usd": 0.043,
    "mock": false
  }
}

Turn Details

1. System turn: Sets the senior security engineer persona, enforces structured <think>...</think> execution, and specifies single-block JSON formatting for tool calls.
2. User turn: Structured prompt specifying domain, subtopic, task specification, and technical scenario.
3. Assistant turn: Opens with an internal <think>...</think> deduction trace analyzing root cause mechanics, memory layouts, or protocol invariants, followed by:
   • Technical analysis, vulnerability diagnosis, and unified diff fix, or
   • A single JSON tool invocation inside a standard code fence:

     ```json
     {"name": "<tool_name>", "arguments": {"<param>": "<val>"}}
     ```
     

Composition

Domains (17)

13 focused cybersecurity domains plus 4 systems engineering calibration domains.

Domain	Records	Focus
software_engineering	79	Concurrency, memory safety, API design, race conditions
soc_threat_intel	76	Log analysis, IOC triage, detection rules, alert correlation
it_platform	68	Access control, container security, IAM, hardening
computer_engineering	64	CPU architecture, cache lines, memory ordering, bit manipulation
reverse_engineering	63	Binary analysis, Ghidra/IDA triage, disassembly, calling conventions
memory_corruption	62	Stack/heap buffers, use-after-free, format strings, ROP/ASLR
programming_general	61	Systems programming, pointer arithmetic, socket programming
os_security	59	Linux kernel, syscall filtering, eBPF, LSMs, privilege escalation
mobile_security	58	Android/iOS reverse engineering, IPC, keychain, permissions
network_security	55	Protocol analysis, TLS handshakes, packet capture, egress rules
owasp_vulnerabilities	55	Top 10 vulnerabilities, auth bypass, IDOR, SSRF, injection
penetration_testing	54	Service enumeration, privilege checks, lateral movement
security_architecture	52	Trust boundaries, zero trust, key management, crypto design
browser_exploitation	50	V8 heap internals, DOM XSS, prototype pollution, CSP
cwe_weaknesses	49	CWE mapping, flaw enumeration, root-cause remediation
devsecops	48	CI/CD pipeline security, SAST/DAST, artifact signing, secret scanning
web_security	44	Web exploitation mechanisms, session fixation, CORS, headers

Task Types (6)

Task	Records	Objective
defense	183	Layered defensive design (prevent, detect, respond) with concrete controls
quiz	181	Technical Q&A targeting common pitfalls, edge cases, and protocol internals
tool_call	175	Single-tool invocation targeting diagnostics, scanning, and analysis tools
analysis	169	Root-cause vulnerability mechanics, protocol teardowns, and invariant inspection
code_review	160	Vulnerability enumeration with CWE identifiers and short unified diff fixes
exploit_walkthrough	129	Step-by-step exploit primitive mechanics, preconditions, and defensive framing

Loading

from datasets import load_dataset

ds = load_dataset("echel0nn1881/kimi-cyber-reasoning", split="train")
print(f"Loaded {len(ds)} records")
print("Domain:", ds[0]["domain"], "| Task:", ds[0]["meta"]["task"])
print("Assistant sample:\n", ds[0]["messages"][2]["content"][:250])

Metadata Fields

Field	Type	Description
seed	string	Technical seed prompt used to initialize generation
subtopic	string	Fine-grained subject area within the primary domain
task	string	Task category (one of the 6 canonical types)
prompt_tokens	integer	Exact input token count recorded by the teacher API
completion_tokens	integer	Exact completion token count recorded by the teacher API
cached_tokens	integer	Prompt tokens served from teacher cache
cost_usd	float	API execution cost recorded for the sample
mock	boolean	false for all records (all samples generated via real API calls)

Curation & Provenance

• Teacher: Generated via API calls to Kimi K3 (Moonshot AI).
• Post-processing: Reasoning traces were processed to remove language deliberation preambles and instruction hierarchy debates, preserving 100% of technical deduction, disassembly, code snippets, and tool parameters.
• Scope: Technical grounding spans both vulnerability mechanics (heap layouts, shellcode framing, ROP chains, injection vectors) and defensive countermeasures (compiler mitigations, kernel hardening, secure architecture).
• License: Released under WTFPL without downstream commercial restrictions.