Agenthound – Offensive security framework for AI agent infrastructure

AgentHound, an open-source offensive security framework for AI agent infrastructure, has been released. The tool performs recon, fingerprinting, credential looting, model inversion, and persistence across the agentic stack, merging findings into a Neo4j graph to prove attack paths. It covers 12 agent clients, MCP, A2A, model gateways, inference servers, vector stores, MLOps, and notebooks.

MCP · A2A · model gateways · inference servers · vector stores · MLOps · notebooks · 12 agent clients Quickstart -quick-start · Capabilities -capabilities · Lifecycle -the-offensive-lifecycle · Graph Model https://docs.agenthound.io/reference/graph-model/ · Docs https://docs.agenthound.io · Safety -safety--authorization 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 inside scan 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 mappings One 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: Homebrew macOS or Linux; adds the tap automatically brew install adithyan-ak/agenthound/agenthound Go 1.25.12+ 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 https://docs.agenthound.io/getting-started/install/ . 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 https://docs.agenthound.io/reference/cli/ 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 https://docs.agenthound.io/reference/detection-rules/ and Risk Scoring https://docs.agenthound.io/reference/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 a get 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 embed Reverter ; 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 disk before the write lands. AUTHORIZED gates + - interactive first-run prompts for looting and offensive actions. IDs are required for --engagement-id extract , poison , implant , and campaign , optional for loot , and recorded on the evidence or receipts those commands emit. Recon guardrails - public-IP targets require --allow-public-targets plus interactive AUTHORIZED ; --authorization-file optionally records a path + SHA-256 watermark. Link-local and multicast targets are refused, except for the explicit cloud-metadata address 169.254.169.254 . 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 https://docs.agenthound.io/operator/security/ and offensive actions guide https://docs.agenthound.io/operator/offensive-actions/ . Quickstart https://docs.agenthound.io/getting-started/quickstart/ · CLI https://docs.agenthound.io/reference/cli/ · Graph Model https://docs.agenthound.io/reference/graph-model/ · Detection Rules https://docs.agenthound.io/reference/detection-rules/ · Security https://docs.agenthound.io/operator/security/ Write your own attack: implement an action interface, drop a register.go , blank-import it - see CONTRIBUTING.md /adithyan-ak/AgentHound/blob/main/CONTRIBUTING.md and the module authoring guide https://docs.agenthound.io/contributing/modules/ . Found a vulnerability in AgentHound itself? See SECURITY.md /adithyan-ak/AgentHound/blob/main/SECURITY.md . AgentHound is licensed under the Apache License 2.0 /adithyan-ak/AgentHound/blob/main/LICENSE .