MCP · A2A · model gateways · inference servers · vector stores · MLOps · notebooks · 12 agent clients
Quickstart · Capabilities · Lifecycle · Graph Model · Docs · Safety
Authorized use only.AgentHound ships read-only discoveryandactive exploitation modules. Run it only against infrastructure you own or are written-authorized to assess. See[Safety & Authorization].
AgentHound is an open-source offensive security framework for AI agent infrastructure. It runs the full engagement - recon, fingerprinting, credential looting, modelfile / system-prompt / fine-tune inventory, model inversion, tool and instruction poisoning, and config-implant persistence - across every layer of the modern agentic stack, then merges every fact into one Neo4j graph and proves the attack paths that tie it all together. Agenthound is BloodHound for the agentic stack.
| 🌐 | 🔓 | | 🧬 | 🔬 | | ☠️ | 🗄️ | | 🕸️ | 🧪 | | 📊 | 🧩 |
| Surface | Discovery & inventory | Validation / active operations |
|---|---|---|
| Agent clients | ||
12 MCP client config formats plus instruction files (CLAUDE.md , AGENTS.md , .cursorrules ) |
||
| Instruction poisoning and reversible malicious-server config implants | ||
| MCP | ||
| Stdio and HTTP/SSE servers, tools, resources, prompts, and authentication | Credential-reach verification; ContextForge tool-description poisoning and round-trip validation | |
| A2A | ||
| Agent cards, JWS verification, skills, delegation, and authentication | Cross-protocol and delegation-path analysis | |
| LiteLLM | ||
| Operator-supplied master-key record, masked provider references, and hashed virtual-key metadata with spend context | Cross-service credential correlation and path analysis | |
| Ollama / vLLM | ||
| Ollama model metadata, stable modelfile hashes, system-prompt presence, and fine-tune signals; vLLM fingerprinting | Optional raw modelfile, template, and system-prompt capture; local GGUF extraction | |
| Qdrant | ||
| Collections, point counts, and optional bounded payload samples | Read-only exposure analysis | |
| MLflow | ||
| Experiments, runs, registered models, artifact/storage URIs, and verified anonymous-exposure evidence | Read-only exposure analysis | |
| Jupyter | ||
| Sessions and bounded notebook trees | Read-only anonymous-versus-authenticated exposure analysis | |
| Open WebUI / LangServe | ||
| Open WebUI authentication posture plus authenticated upstream/RAG credential inventory and observed exposure evidence; LangServe fingerprinting | Read-only credential inventory and exposure evidence |
8 lifecycle CLI commands-scan
·discover
·loot
·extract
·poison
·implant
·revert
·campaign
(enumerate
+fingerprint
run insidescan
)8 fingerprinters · 6 looters · 1 model-inversion extractor · 2 poisoners · 1 implanter****Graph: 23 node labels · 32 edge kinds (20 raw + 12 composite) ·15 post-processors****Intelligence: 35 text-detection rules + 7 YAML fingerprint rules + 1 code-backed Jupyter detector · 19 prebuilt attack-path queries · OWASP MCP Top 10 + OWASP Agentic Top 10 + MITRE ATLAS mappingsOne static collector binary with no DB/UI/server dependencies. Config-only discovery can run offline. Apache-2.0 releases include a Cosign-signed checksum manifest and per-archive SPDX SBOMs.
Default path prerequisites: Docker + Compose v2. No Go, no Node, no
git clone
.
1. Start the analysis server - Neo4j + Postgres + UI, binds
127.0.0.1:8080
:
curl -sSfL https://raw.githubusercontent.com/adithyan-ak/agenthound/main/docker/docker-compose.public.yml | docker compose -f - -p agenthound up -d --wait
2. Install the collector - single static binary → ~/.local/bin
:
curl -sSfL https://raw.githubusercontent.com/adithyan-ak/agenthound/main/install.sh | sh
export PATH="$HOME/.local/bin:$PATH"
Or choose one of these package-manager alternatives:
brew install adithyan-ak/agenthound/agenthound
go install github.com/adithyan-ak/agenthound/collector/cmd/agenthound@1.0.0
Go installs into GOBIN
or, by default, $(go env GOPATH)/bin
; ensure that
directory is on PATH
.
3. Scan local configs - offline, read-only, raw credential values omitted. Choose one coverage level and ingest the saved artifact.
Normal scan — recommended first run:
agenthound scan --config --ingest http://127.0.0.1:8080
Deep scan — adds bounded nested-project instruction discovery:
agenthound scan --config --deep --ingest http://127.0.0.1:8080
Both commands check registered instruction sources at your home and selected
project roots. Add --project-dir /path/to/project
when the target is not the current directory. Deep discovery keeps that selected project independently covered even inside a normally pruned home subtree.
The collector saves ./scan-<scan_id>.json
before upload, then prints a compact
ingest receipt. Use --json
for the full receipt.
4. Open the graph at http://127.0.0.1:8080.
The standalone server binary is also available as
adithyan-ak/agenthound/agenthound-server
through Homebrew. Both binaries are
available from release archives, or from Go at the explicit
@1.0.0
revision. Release archives include a Cosign-signed checksum manifest and per-archive SPDX SBOMs - see the installation guide.
Collection commands write ingest-ready JSON. The quickstart above shows the ingest pattern once.
1. Recon - find the AI estate:
Scan common AI-service ports and fingerprint what responds:
agenthound scan 10.0.0.0/24
Probe likely web ports for MCP and A2A protocol shapes:
agenthound discover 10.0.0.0/24
2. Loot - inventory credential evidence and model metadata:
With LITELLM_MASTER_KEY
set, inventory LiteLLM credential references and spend metadata:
agenthound loot 10.0.0.20:4000 --type litellm \
--master-key "$LITELLM_MASTER_KEY"
Opt in to raw Ollama modelfiles, templates, and system prompts:
agenthound loot 10.0.0.10:11434 --type ollama \
--include-credential-values
Looter types: litellm
, ollama
, openwebui
, mlflow
, qdrant
, jupyter
.
3. Extract - with AI_MODEL_ID
set to an AIModel ID from the graph, invert a locally-available GGUF weight file to recover fine-tune residue:
agenthound extract "$AI_MODEL_ID" --type embedding-invert \
--artifact /path/to/model.gguf --commit --engagement-id ENG-1
4. Validate, exploit, persist + revert - run sanctioned, reversible offensive actions:
With ContextForge authentication configured, run a reversible poison-and-restore round trip against a managed MCP tool:
agenthound campaign \
https://gateway.example/servers/0123456789abcdef0123456789abcdef/mcp \
--scenario mcp-poison-roundtrip --adapter contextforge \
--target-id support-lookup --engagement-id ENG-ROUNDTRIP --commit
Commit a targeted tool-description poison:
agenthound poison \
https://gateway.example/servers/0123456789abcdef0123456789abcdef/mcp \
--type mcp.tool.description --adapter contextforge \
--target-id support-lookup --inject-file payload.txt \
--commit --engagement-id ENG-1
Implant a malicious MCP server entry, then roll the engagement back:
agenthound implant localhost --type mcp.config.malicious-server \
--file "$HOME/.cursor/mcp.json" --inject-file server-entry.json \
--commit --engagement-id ENG-1
agenthound revert ENG-1
5. Analyze - pathfind and review:
curl -sSf http://127.0.0.1:8080/api/v1/analysis/prebuilt/credential-chain
curl -sSf 'http://127.0.0.1:8080/api/v1/analysis/findings?severity=critical'
See the full CLI reference for every verb, flag, and module.
AgentHound's findings are built around the questions red teams and defenders ask when they need to understand reachability, blast radius, and pathing risk.
| Finding | What it means | Question it answers |
|---|---|---|
| Credential-chain paths | ||
| The same secret appears in multiple contexts, letting trust cross service boundaries. | Which reused credential gives an agent access it never explicitly had? | |
| Reachability | ||
| Agents, MCP servers, tools, resources, prompts, A2A skills, and AI services are joined into one graph. | What can this agent actually reach if trust edges are followed? | |
| Execution paths | ||
| An agent can reach shell-like, database, network, or other high-impact tools. | Which agents have a path to command execution, data-plane control, or production impact? | |
| Exfiltration paths | ||
| An agent can read sensitive data and also reach an outbound channel. | Where can sensitive data leave the environment? | |
| Cross-protocol pivots | ||
| MCP, A2A, host context, and AI-service infrastructure combine into one reachable path. | Can one agent protocol become a bridge into another trust domain? | |
| Tool poisoning | ||
| Tool descriptions, prompts, or instruction files contain suspicious model-steering content. | Which tools or instructions could influence model behavior in unsafe ways? | |
| Tool shadowing | ||
| A lookalike tool mimics a trusted capability or name. | Which tool could intercept or hijack an expected action? | |
| Rug pulls | ||
| A tool's description, schema, or server instructions changed between scans. | What changed since the last known-good graph, and did it create a new risk path? | |
| Unauthenticated servers or agents | ||
| MCP servers or A2A protocol handlers affirmatively accepted a credential-free probe; A2A uses a bounded read-only nonexistent-task lookup and never submits a message. | Which exposed agent surfaces need immediate review? | |
| Risk hotspots | ||
| Nodes and paths are prioritized with risk scores and prebuilt graph queries. | Where should investigation or remediation start first? |
See Detection Rules and Risk Scoring for the full catalog.
AgentHound doesn't just list findings - it creates graph edges you can chain, query, and report:
: an agent can traverse trust, credential, host, or protocol relationships to reach a target.CAN_REACH
: an agent can reach a tool capable of command, database, network, or code execution.CAN_EXECUTE
: an agent can read sensitive data and send it through an outbound channel.CAN_EXFILTRATE_VIA
: an A2A agent can act as another A2A agent.CAN_IMPERSONATE
: a tool mimics a trusted tool closely enough to hijack expected behavior.SHADOWS
: tool or instruction text contains model-steering content.POISONED_DESCRIPTION
/POISONED_INSTRUCTIONS
These edges turn AI-agent infrastructure into something you can pathfind instead of manually reason about.
flowchart LR
Agent["AgentInstance<br/>claude-desktop"]
Notes["MCPServer<br/>internal-notes"]
Identity["Identity<br/>configured auth"]
ConfigCred["Credential<br/>configured secret<br/>value_hash: a3f9..."]
Gateway["LiteLLMGateway<br/>prod"]
MasterCred["Credential<br/>gateway master key<br/>value_hash: a3f9..."]
ProviderRef["Credential<br/>masked provider reference<br/>material not observed"]
Agent -- TRUSTS_SERVER --> Notes
Notes -- AUTHENTICATES_WITH --> Identity
Identity -- USES_CREDENTIAL --> ConfigCred
ConfigCred -. "same value_hash<br/>correlation evidence, not a stored edge" .-> MasterCred
Gateway -- EXPOSES_CREDENTIAL --> MasterCred
Gateway -- EXPOSES_CREDENTIAL --> ProviderRef
Agent -- "CAN_REACH<br/>(derived)" --> ProviderRef
No single config file declares this path. AgentHound hashes the supplied
LiteLLM master key, correlates it with the matching client-config credential by
value_hash
, and computes the derived reachability edge once both outputs land in the same graph. The dotted correlation is explanatory, not a stored relationship. The provider target remains a reference-only finding: it does not assert that AgentHound obtained usable upstream provider secret material.
Built to be run under authorization, with the controls this audience checks for:
Read-only looter contract- GET/HEAD by default, with documented lookup/search POSTs for APIs that expose no read equivalent and an opt-in Ollama embeddings compute POST via--include-embeddings
; each looter is guarded by aget_only_test.go
regression test.Mutating verbs dry-run by default-poison
,implant
, and mutation campaigns do not modify a target without--commit
.extract
performs its local analysis in dry-run and uses--commit
only to emit ingest data.Compile-time-mandatory recovery path-Poisoner
/Implanter
embedReverter
; every destructive module must implement recovery. Runtime restoration is verified, not guaranteed across provider policy changes, conflicts, or unavailable targets.Receipt before mutation- the undo receipt is persisted to diskbeforethe write lands.AUTHORIZED gates +- interactive first-run prompts for looting and offensive actions. IDs are required for--engagement-id
extract
,poison
,implant
, andcampaign
, optional forloot
, and recorded on the evidence or receipts those commands emit.Recon guardrails- public-IP targets require--allow-public-targets
plus interactiveAUTHORIZED
;--authorization-file
optionally records a path + SHA-256 watermark. Link-local and multicast targets are refused, except for the explicit cloud-metadata address169.254.169.254
.
It is explicitly not a C2, a stealth/evasion implant, or a multi-user SaaS. It is transparent, single-user authorized-assessment tooling, and the design says so.
Read the security posture guide and offensive actions guide.
Quickstart · CLI · Graph Model · Detection Rules · Security
Write your own attack: implement an action interface, drop a register.go
, blank-import it - see CONTRIBUTING.md and the module authoring guide. Found a vulnerability in AgentHound itself? See SECURITY.md.
AgentHound is licensed under the Apache License 2.0.