AI Space
AI Space is a sanitized challenge note from the local HTB archive, organized for quick review by category, difficulty, evidence flow, and reusable operator
Scenario
AI Space attack path
AI Space is a sanitized challenge note from the local HTB archive, organized for quick review by category, difficulty, evidence flow, and reusable operator
Objective
Challenge walkthrough focused on AI-ML evidence, validation, and reusable operator lessons.
Walkthrough flow
Artifact review
Hypothesis
Validated solve path
Proof captured
Source coverage
High source coverage
Status: complete. This article is generated from 6 sanitized Markdown sources and keeps raw flags, credentials, keys, cookies, and reusable secrets out of the rendered blog.
High confidence: the page is reconstructed from a primary walkthrough plus multiple supporting notes or evidence sources. Treat the chain as source-backed, while still checking the listed source files for sensitive values.
- AI-ML/AI-Space/writeup.md
- htb-challenge/AI-ML/AI-Space/notes.md
- htb-challenge/AI-ML/AI-Space/memory-summary.md
- htb-challenge/AI-ML/AI-Space/hypothesis-board.md
- HTB/_knowledge/exports/ctf-lightrag-latest-203412/documents/challenge__AI-ML__AI-Space__memory-summary.md.c86cfb0a67.md
- HTB/_knowledge/exports/ctf-lightrag-latest-203412/documents/challenge__AI-ML__AI-Space__notes.md.0baeb09cc7.md
Technical Walkthrough
Writeup
Challenge
- Name: AI-Space
- Category: AI-ML
- Difficulty: Easy
- Mode: file
Summary
The challenge archive contains one NumPy pairwise distance matrix. The matrix encodes a set of 2D points whose layout is recoverable with classical multidimensional scaling. Once projected back into two dimensions and plotted, the points visually spell the flag.
Artifact Inventory
files/a12c733a-a7dd-47d2-872e-2f7ab5569df6.zip: original challenge archive.files/extracted/distance_matrix.npy: extracted1808 x 1808float64 pairwise distance matrix.analysis/artifact-inventory.json: archive hash, size, and contents.analysis/matrix-overview.txt: matrix invariants and nearest-neighbor statistics.analysis/mds-overview.txt: classical MDS eigenvalue and coordinate summary.
Analysis
The scenario talks about coordinates and signal origins, and the only extracted artifact is a distance matrix. Initial inspection showed a finite symmetric matrix with a zero diagonal, which is the expected shape for pairwise distances.
Classical multidimensional scaling was the natural validation path. Double-centering the squared distances produced a Gram matrix with two dominant positive eigenvalues and only numerical noise afterward, confirming the data is effectively two-dimensional. Plotting those two recovered dimensions produced readable HTB-format text.
Because the rendered plot visibly contains the flag, generated images were stored under loot/ rather than analysis/.
Solve
Run:
cd <local workspace>
AI-ML/AI-Space/.venv/bin/python AI-ML/AI-Space/solve/solve.pyThe solver loads distance_matrix.npy, reconstructs coordinates with classical MDS, and writes a readable scatter plot to loot/reconstructed-flag.png. The OCR result from that plot was captured through the harness into loot/flag.txt.
Flag
Raw flag is stored in loot/flag.txt and intentionally not reproduced here.
Lessons
When an AI/ML challenge provides only a distance matrix, inspect matrix invariants before assuming a trained model is involved. A rank-2 Euclidean distance matrix is often enough to recover hidden coordinates directly with classical MDS, and the resulting scatter may encode the answer visually.
Source-Backed Dossier
The sections below are merged from companion Markdown notes for the same case. They are rendered after sanitization so the article stays precise without publishing raw flags, credentials, or target-specific secrets.
Notes
Scope
- Challenge: AI-Space
- Category: AI-ML
- Difficulty: Easy
- Mode: file
- Remote instance: none
- Start time: 2026-06-09T08:18:07Z
- Operator: harness
- State file:
challenge-state.json
Harness Status
- Current phase: see
challenge-state.json - Next allowed actions: see
next-action.json - Raw flags and sensitive material stay in
loot/only. Do not paste them here.
Artifact Inventory
| File | Size | SHA256 | Type | Notes |
|---|---|---|---|---|
files/a12c733a-a7dd-47d2-872e-2f7ab5569df6.zip | 24631248 | <hash redacted> | Zip archive data, at least v2.0 to extract, compression method=deflate | zip entries: 1 shown in artifact inventory JSON |
Evidence Ledger
| Time | Action | Output/File | Finding | Confidence | Next |
|---|---|---|---|---|---|
| 2026-06-09T08:18:07Z | harness init | challenge-state.json | Workspace initialized with deterministic state file | High | Inventory artifacts |
| 2026-06-09T08:18:07Z | artifact inventory | analysis/artifact-inventory.json | 1 artifact(s) inventoried | High | Build or update hypotheses |
| 2026-06-09T08:18:39Z | hypothesis recorded | hypothesis-board.md | Recover a hidden path or ordering from distance_matrix.npy, likely by treating it as a graph/distance matrix and mapping the resulting route or clusters to ASCII/flag text. | Medium | Load the NumPy matrix, inspect shape/symmetry/value distribution, then test nearest-neighbor/MST/TSP-style ordering and decode candidate route outputs. |
| 2026-06-09T08:22:36Z | flag capture | loot/flag.txt | HTB-format flag captured; raw value kept in loot only | High | Write solution and run completion gate |
| 2026-06-09T08:23:15Z | research record | analysis/research/research-records.md | Research tagged MATCHED | Medium | Validate against current evidence |
| 2026-06-09T08:23:16Z | completion gate | challenge-state.json | Completion gate passed; state marked COMPLETE | High | Optional sanitized memory summary approval |
Key Findings
-
RAG / Advisory Memory
RAG output is advisory only. Record evaluated retrievals with:
scripts/challenge_harness.py rag-record <workspace> --query "..." --tag MATCHED|PARTIAL|MISSING|<secret redacted>|GENERIC --validation "..."Secrets/Flags
Raw flags and sensitive material stay in loot/ only. Use scripts/challenge_harness.py capture-flag to validate and record flag capture without printing the value.
Memory Summary
Metadata
- Platform: HackTheBox Challenges
- Category: AI-ML
- Challenge: AI-Space
- Difficulty: Easy
- Source workspace:
<local workspace>
Validated Solve Chain
Concepts only. Do not include raw flags, reusable credentials, tokens, cookies, private keys, or live secrets.
1.
Reusable Lessons
-
Dead Ends
-
Tool Quirks
-
Evidence Paths
-
Ingestion Decision
- Proposed for LightRAG: yes/no
- Requires user approval before ingestion: yes
Hypothesis Board
Keep no more than 3 active hypotheses on Easy/Medium and 5 on Hard unless the user explicitly asks for breadth.
| Rank | Path | Evidence | Missing Proof | Cheapest Validation | Confidence | Status |
|---|---|---|---|---|---|---|
| 1 | Recover a hidden path or ordering from distance_matrix.npy, likely by treating it as a graph/distance matrix and mapping the resulting route or clusters to ASCII/flag text. | Challenge scenario mentions coordinates, signal origins, hideouts, and tracking algorithms; ZIP contains only distance_matrix.npy. | Load the NumPy matrix, inspect shape/symmetry/value distribution, then test nearest-neighbor/MST/TSP-style ordering and decode candidate route outputs. | Medium | Active |
Closed Branches
| Branch | Evidence Tested | Failure Output | Reason Closed | Revisit Condition |
|---|
Memory Summary
approval_required: true
Sanitized Memory Summary
Metadata
- Platform: HackTheBox Challenges
- Category: AI-ML
- Challenge: AI-Space
- Difficulty: Easy
- Source workspace:
<local workspace>
Validated Solve Chain
Concepts only. Do not include raw flags, reusable credentials, tokens, cookies, private keys, or live secrets.
1.
Reusable Lessons
-
Dead Ends
-
Tool Quirks
-
Evidence Paths
-
Ingestion Decision
- Proposed for LightRAG: yes/no
- Requires user approval before ingestion: yes
Notes
Notes
Scope
- Challenge: AI-Space
- Category: AI-ML
- Difficulty: Easy
- Mode: file
- Remote instance: none
- Start time: 2026-06-09T08:18:07Z
- Operator: harness
- State file:
challenge-state.json
Harness Status
- Current phase: see
challenge-state.json - Next allowed actions: see
next-action.json - Raw flags and sensitive material stay in
loot/only. Do not paste them here.
Artifact Inventory
| File | Size | SHA256 | Type | Notes |
|---|---|---|---|---|
files/a12c733a-a7dd-47d2-872e-2f7ab5569df6.zip | 24631248 | <hash redacted> | Zip archive data, at least v2.0 to extract, compression method=deflate | zip entries: 1 shown in artifact inventory JSON |
Evidence Ledger
| Time | Action | Output/File | Finding | Confidence | Next |
|---|---|---|---|---|---|
| 2026-06-09T08:18:07Z | harness init | challenge-state.json | Workspace initialized with deterministic state file | High | Inventory artifacts |
| 2026-06-09T08:18:07Z | artifact inventory | analysis/artifact-inventory.json | 1 artifact(s) inventoried | High | Build or update hypotheses |
| 2026-06-09T08: <REDACTED>, likely by treating it as a graph/distance matrix and mapping the resulting route or clusters to ASCII/flag text. | Medium | Load the NumPy matrix, inspect shape/symmetry/value distribution, then test nearest-neighbor/MST/TSP-style ordering and decode candidate route outputs. | |||
| 2026-06-09T08: <REDACTED> | |||||
| 2026-06-09T08:23:15Z | research record | analysis/research/research-records.md | Research tagged MATCHED | Medium | Validate against current evidence |
| 2026-06-09T08:23:16Z | completion gate | challenge-state.json | Completion gate passed; state marked COMPLETE | High | Optional sanitized memory summary approval |
Key Findings
-
RAG / Advisory Memory
RAG output is advisory only. Record evaluated retrievals with:
scripts/challenge_harness.py rag-record <workspace> --query "..." --tag MATCHED|PARTIAL|MISSING|<secret redacted>|GENERIC --validation "..."Secrets/Flags
Raw flags and sensitive material stay in loot/ only. Use scripts/challenge_harness.py capture-flag to validate and record flag capture without printing the value.
Technical analogy
How to remember this solve
Think of the challenge as a small system with one rule that matters more than the rest. The solve is finding that rule, validating it, and using it carefully enough to reach the final proof.
For AI Space, keep the mental model simple: identify the trusted assumption, prove it with the smallest safe test, then automate or repeat only the part that directly leads to the flag.