If your team is building or evaluating an AI agent security tool — a prompt-injection scanner, a firewall for tool calls, an anomaly detector for agent behavior — you now have a genuinely open, reproducible way to measure it. A new benchmark corpus called Contemporary Agent Attacks just landed on GitHub, and it’s built specifically for this kind of testing.

What’s in the Corpus

Per the official GitHub repository, the corpus contains:

  • 497 attacks (395 visible + 102 holdout) across 13 categories
  • 1,172 benign samples across 4 negative categories, for measuring false-positive rates
  • A tool-agnostic benchmark runner that works with any HTTP-addressable classifier
  • Licensed CC-BY 4.0 — “use it for your product, paper, or class”

The README describes the scope directly: “The corpus is aimed at contemporary agent-shaped threats: prompt injection, credential exfiltration, indirect injection via tool output and RAG, tool abuse, supply-chain manipulation, memory poisoning, and agent-to-agent compromise.”

That’s a notably broader threat surface than older prompt-injection-only benchmarks — it explicitly covers RAG-mediated attacks, supply-chain manipulation, and agent-to-agent compromise, which map to real failure modes in multi-agent and tool-using systems rather than just single-turn jailbreaks.

The Holdout Split Matters

One detail worth calling out for anyone building a detector, not just evaluating one: the repository maintains a strict holdout split. Per the README: “Files prefixed with holdout_ are reserved for evaluating generalization; they were collected from sources not used to construct the visible corpus and have never been public before this release. If you train on this corpus, do not train on holdout files — use them only for final scoring.”

If you’re fine-tuning or building rules based on the visible corpus, respect that split. Using the holdout set for training defeats the entire point of having a generalization check, and any results you publish using holdout-trained detection would be misleading.

Running the Benchmark

The repository provides two supported entry points, both confirmed directly from the official README’s quick-start section:

Shell runner, pointed at any HTTP scan endpoint:

git clone https://github.com/AndrewSispoidis/contemporary-agent-attacks
cd contemporary-agent-attacks

# Point at whatever classifier you want to measure
ENDPOINT=http://127.0.0.1:7749/v1/firewall/scan/full ./benchmark/run.sh

Python runner, which adds a per-category breakdown and JSON output:

python3 benchmark/run.py \
  --endpoint http://127.0.0.1:7749/v1/firewall/scan/full \
  --tool my-tool \
  --output benchmark/results/my-tool.json

Per the README, the endpoint requirement is minimal: “a scan endpoint that accepts POST {"text": "<content>"} and returns a JSON body indicating blocked vs. allowed.” Requirements are Python 3.9+, and jq if you’re using the shell runner specifically.

Writing a Custom Adapter

If your tool doesn’t expose a simple HTTP scan endpoint, the repository supports adapters instead. Per the README: write “a ~40-line adapter for your tool” — there’s a reference example at benchmark/adapters/crawdad.py — and then run:

python3 benchmark/run.py --adapter adapters.my_tool --tool my-tool

How Scoring Works

The corpus layout is intentionally minimal for portability: “Each attack/negative is one .txt file with a descriptive slug filename. The files contain only the attack or benign text — no metadata, so you can feed them to any tool without parsing.” That design choice means you can point essentially any classifier at the corpus without writing a custom parser first.

For the exact scoring methodology, sourcing, and collection details, the README points to a dedicated METHODOLOGY.md file in the repo — worth reading in full before you publish results, since it documents exactly how attacks were sourced and how detection/false-positive scoring is defined.

A Published Baseline

The repository’s README currently lists one entry on its results table: crawdad-v0.9.1 at 99.80% detection, 0.00% false-positive rate, and 99.90% F1, dated 2026-04-20. The full leaderboard lives at leaderboard.md, with raw JSON results in the benchmark/results/ directory — both worth checking before you run your own numbers, so you have a sense of where the current state of the art sits.

Why This Is Worth Your Time

Security tooling for agentic AI has a citation problem: vendors publish detection-rate claims against private, non-reproducible test sets, making apples-to-apples comparison nearly impossible. An open, CC-BY 4.0, holdout-protected corpus with a documented methodology changes that dynamic — you can run the same 497 attacks and 1,172 benign samples against your own tool and a competitor’s, and actually compare the numbers honestly.

If you’re building or buying agent security tooling, this is worth adding to your evaluation checklist today.

Sources

  1. Contemporary Agent Attacks — Official GitHub Repository

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260816-0800

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