sumitguha13/adr-agent-trace-detection

ADR Agent Trace Detection

Paired benign/malicious AI-agent execution traces for training detectors of compromised agent behaviour. Built to evaluate against Uber's ADR-Bench, which is used strictly for validation and contributed nothing to this data.

Contrastive paired design

1951 pairs. Each pair is one scenario rendered twice — once clean, once compromised — holding constant everything a detector could cheat on:

Held identical within a pair	Differs
MCP servers offered	whether the agent is subverted
exact tool call sequence and order	what tool results contain
number of tool calls and messages	what the agent does with them
user request length band

The point is that which tools get called carries no label information. Only how they are used does.

Shortcut audit — including what is still wrong

A classifier was trained on surface features alone to check whether the label is recoverable without reading behaviour. Only fields ADR actually renders into a prompt were tested (description, category and timestamps never reach the model, so probing them measures a leak nothing can exploit).

Probe	Accuracy	Verdict
tool/server vocabulary	0.500	clean — chance
task category	0.500	clean — chance
structural / length statistics	0.761	LEAK — see below
trace content (tf-idf)	0.911	the intended signal

Known confound. Tool results in malicious traces run longer, and length statistics recover the label at 76%. This survived three regeneration rounds:

Round	Fix applied	Result
1	independent sampling per class	structure 0.740, vocab 0.611
2	paired skeletons (shared structure)	structure 0.600, vocab 0.615
3	pinned tool sequences	vocab 0.500, structure 0.761
4	per-step length budgets	not run — generation budget exhausted

Post-hoc mitigation was attempted and failed: truncating results at five caps left structure at 0.675–0.766, and filtering to length-matched pairs left 0.679 while discarding 84% of data.

On ADR-Bench the length correlation runs the opposite direction (malicious traces are shorter), so a length-reliant model should do badly there. Measured result: the model trained on this data reaches 72.7% balanced accuracy on ADR-Bench vs a 54.9% base-model baseline, so content signal dominated in practice. The confound is real but was not fatal.

Configs

• traces — 1951 pairs, benign and malicious side by side.
• benchmark — 3902 task records in UberADR ADR-Bench wire format (manifest keys, mcp__server__tool naming, toolu_+24 call ids, Python-repr tool results), verified field-for-field against real ADR-Bench records.
• train / validation — 3512 / 390 examples, split by pair so a benign trace and its malicious twin never straddle the split.

Build method

• Tool catalog: 178 MCP servers / 1,126 tools across 25 domains, seeded on real servers from MCPToolBench++.
• Attack language: 440 real injection strings mined from NVIDIA Nemotron-AIQ-Agentic-Safety. Its traces were deliberately not imported — that dataset is malicious-only and uses a 3-function vocabulary, which would have separated the classes by vocabulary alone.
• Coverage: all 17 ADR threat techniques.
• Generated across six Gemini model families, assigned per pair so generator identity cannot correlate with label.

Limitations

• Synthetic traces; real-world attacks may present differently.
• The length confound above is unfixed.
• Semantic Data Poisoning transfers poorly (0/4 on ADR-Bench) — the synthetic examples do not capture it.