GenAI discovery at Techno West 2025: DFIR collection, artifacts, and authenticity workflows

4n6 Beat
7 min read

Techno Security & Digital Forensics Conference West 2025 kicks off in San Diego on October 27–29 at the Town & Country Resort, with a strong emphasis on Generative/Agentic AI discovery and legal impacts (event announcement, program highlights). Legal-oriented sessions are explicitly tackling discovery for GenAI and agentic AI, including JAMS’ panel “Artificial Intelligence and Generative AI: Causes of Action and Defenses and Discovery” scheduled for Monday, October 27 at 3:15 p.m. (JAMS session page). Regional partners also underline the AI-heavy tracks (Cybersecurity, eDiscovery, Forensics, Investigations) running October 27–29 (CCOE event listing).

Courts and policy are still catching up to synthetic media and AI-assisted workflows, raising stakes for DFIR teams to capture the right evidence at the outset. Recent coverage and proceedings highlight gaps in courtroom readiness and rulemaking for deepfakes and AI-derived evidence (Axios, Ars Technica).

What DFIR teams should watch at Techno West

  • eDiscovery/AI crossovers: discovery of model/agent artifacts, legal defenses, and proportional preservation for GenAI outputs (JAMS session).
  • Operational AI topics embedded in investigations (e.g., AI in LE use, AI transparency case law) reflected on Monday’s program (program listing).

Evidence categories you will encounter in GenAI/Agentic investigations

  1. Local LLM/agent runtimes and caches (workstations, lab hosts)
  • Ollama models and runtime:
    • Default model paths: macOS ~/.ollama/models, Linux /usr/share/ollama/.ollama/models, Windows C:\Users\%username%\.ollama\models (Ollama FAQ).
    • Default API bind: 127.0.0.1:11434 (changeable via OLLAMA_HOST) (Ollama FAQ).
  • LM Studio chats/models:
    • Conversation JSONs: macOS/Linux ~/.lmstudio/conversations/, Windows %USERPROFILE%\.lmstudio\conversations (LM Studio docs).
    • LM Studio exposes OpenAI‑compatible endpoints locally (e.g., http://localhost:1234/v1) for integrations (LM Studio OpenAI‑compat API).
  • Hugging Face caches commonly present when models/datasets are pulled:
    • Hub cache default: ~/.cache/huggingface/hub; datasets: ~/.cache/huggingface/datasets (configurable via HF_HOME, HF_HUB_CACHE, HF_DATASETS_CACHE) (HF Hub cache guide, HF Datasets cache).
  • Common agent/dev frameworks leave local state:
  1. Cloud AI usage and enterprise logs
  1. AI‑generated media and provenance metadata

Practical collection checklists and artifact paths

Use these as ready-to-run steps during triage or search execution.

A. Identify and collect local LLM and agent artifacts

  • Ollama (models, manifests)
    • Paths: ~/.ollama/models (macOS), /usr/share/ollama/.ollama/models (Linux), C:\Users\%username%\.ollama\models (Windows) (Ollama FAQ).
    • Runtime detection: process binding to 127.0.0.1:11434 by default (Ollama FAQ).
  • LM Studio (chats)
    • Conversations: macOS/Linux ~/.lmstudio/conversations/; Windows %USERPROFILE%\.lmstudio\conversations (LM Studio docs).
  • Hugging Face caches (models/datasets pulled by many apps)
    • Hub: ~/.cache/huggingface/hub (or HF_HUB_CACHE); Datasets: ~/.cache/huggingface/datasets (or HF_DATASETS_CACHE) (HF Hub cache, HF Datasets cache).
  • LangChain agent caches
  • LlamaIndex indices/stores
    • If storage_context.persist() used, default is ./storage unless persist_dir provided (LlamaIndex save/load).

Suggested triage commands:

# macOS/Linux: enumerate common GenAI artifacts for current user
ls -la ~/.ollama/models ~/.lmstudio/conversations ~/.cache/huggingface/hub ~/.cache/huggingface/datasets 2>/dev/null

# Windows PowerShell (run as user):
Get-ChildItem "$env:USERPROFILE\.ollama\models" -Recurse -ErrorAction SilentlyContinue
Get-ChildItem "$env:USERPROFILE\.lmstudio\conversations" -Recurse -ErrorAction SilentlyContinue
Get-ChildItem "$env:USERPROFILE\.cache\huggingface" -Recurse -ErrorAction SilentlyContinue

# Find likely local AI servers (LM Studio ~1234, Ollama 11434)
# macOS/Linux:
sudo lsof -iTCP -sTCP:LISTEN | egrep ':11434|:1234'
# Windows (PowerShell):
Get-NetTCPConnection -LocalPort 11434,1234 -State Listen | Ft -AutoSize

YARA indicator for GGUF model files (commonly used by llama.cpp/Ollama):

rule GGUF_Model_File {
  meta:
    description = "Detects GGUF model files by magic bytes"
    reference = "GGUF spec header magic per llama.cpp"
  strings:
    $gguf = {47 47 55 46}  // ASCII 'GGUF'
  condition:
    uint32(0) == 0x46554747 or $gguf at 0
}

This relies on the documented GGUF magic header “GGUF” at file start (llama.cpp gguf.h).

B. Preserve cloud usage and org logs

  • Request exports via enterprise interfaces where available:
  • Network telemetry: capture DNS/HTTPS metadata for api.openai.com, api.anthropic.com, and generativelanguage.googleapis.com to corroborate usage (Anthropic docs, Gemini API). Typical OpenAI client defaults target https://api.openai.com/v1 (Open WebUI guide, Kani client default).

C. Verify AI-generated media and provenance

  • Always acquire originals (no re-encodes). Compute cryptographic hashes on intake.
  • Check for Content Credentials/C2PA:
  • Test for watermarks when applicable:
    • Google’s SynthID Detector portal for media created with Google models (early access) (Google blog). Adoption and platform enforcement vary; treat watermarking as one signal, not dispositive (The Verge overview).

Detection ideas (SOC/IR rules of thumb)

  • Host detections
    • Process listening on 127.0.0.1:11434 with concurrent I/O to ~/.ollama/models → flag local LLM runtime (Ollama) (Ollama FAQ).
    • File creations >1–20 GB in ~/.ollama/models/blobs or %USERPROFILE%\.ollama\models\blobs over short intervals → large model pulls (Ollama FAQ).
    • Frequent writes to .langchain.db or ./storage/ in project folders → active agent pipelines (LangChain/LlamaIndex) (LangChain SQLiteCache, LlamaIndex persistence).
    • Presence of .lmstudio/conversations/*.json steadily increasing → local chat usage (LM Studio docs).
  • Network detections
    • Egress to api.openai.com/api.anthropic.com/generativelanguage.googleapis.com with POSTs of JSON payloads → API usage corroboration (Anthropic, Gemini).
    • Local web traffic to http://localhost:1234/v1 or http://127.0.0.1:11434/api from desktop apps → local model proxies (LM Studio, Ollama) (LM Studio OpenAI‑compat API, Ollama FAQ).
  • Expect courts to scrutinize authenticity for AI media; guidance is evolving. Panels have debated but not finalized new Rules changes; judges have questioned whether existing authentication rules suffice, at least for now (Ars Technica). Practical readiness concerns persist (Axios).
  • For chat/agent evidence, capture:
    • Prompt history (system/user), tool calls/actions, files referenced, model ID/version/quantization, parameters (temperature/top‑p), plugins/extensions used.
    • Workspace and org logs where available (e.g., OpenAI Compliance API; Admin/Audit Logs) (OpenAI Compliance API, Admin/Audit help).
  • For AI‑generated images/audio/video, include C2PA verification output in reports and note verifier trust lists and statuses (C2PA explainer, Verify/ITL).

Sample response playbook (first 24–48 hours)

  1. Scoping and containment
  1. Forensic acquisition
  1. Analysis and reporting
  • Correlate local artifacts with org logs and network telemetry (hosts api.openai.com, api.anthropic.com, generativelanguage.googleapis.com) (Anthropic, Gemini).
  • Document chain of prompts, tools, and model settings; include verifier outputs and trust-list references for any Content Credentials (C2PA explainer, ITL).

Quick reference snippets

Enumerate and hash LM Studio chats (macOS/Linux):

find ~/.lmstudio/conversations -type f -name '*.json' -print0 | xargs -0 shasum -a 256

Verify C2PA manifest with trusted anchors:

export C2PATOOL_TRUST_ANCHORS='https://contentcredentials.org/trust/anchors.pem'
export C2PATOOL_ALLOWED_LIST='https://contentcredentials.org/trust/allowed.sha256.txt'
export C2PATOOL_TRUST_CONFIG='https://contentcredentials.org/trust/store.cfg'

c2patool suspect.jpg trust

(c2patool usage).

List local Ollama models and inspect on-disk size:

ollama list
du -sh ~/.ollama/models 2>/dev/null || sudo du -sh /usr/share/ollama/.ollama/models

(Ollama FAQ).

Research signal: detection is an arms race

Academic and government R&D emphasize evolving, adaptive detection and provenance rather than static fingerprints alone (Azizpour et al., 2025, DARPA SemaFor transition notes). Reviews also warn about adversarial fragility of many detectors (Khan et al., 2025, AFSL robustness paper). Treat any single method (including watermarks) as probabilistic signal, not proof (The Verge on adoption gaps, Google SynthID Detector blog).

Takeaways

  • Add local LLM/agent paths to standard triage: Ollama models, LM Studio chats, HF caches, .langchain.db, and LlamaIndex ./storage.
  • Monitor for local AI servers on 11434 (Ollama) and 1234 (LM Studio) and for cloud hosts api.openai.com, api.anthropic.com, and generativelanguage.googleapis.com.
  • Use enterprise APIs to export AI usage logs (OpenAI Compliance/Admin/Audit) early in an investigation.
  • Validate media provenance with C2PA tools/Verify, and treat watermarks as one signal among many.
  • For reports, capture prompts, tools, model versions/quantization, and verifier trust context to support courtroom scrutiny.

Sources / References