Invaris AgentSec

Adversarial security and reliability testing for autonomous AI agents.

Invaris AgentSec is an open-source testing framework for finding unsafe, unauthorized, and unreliable agent behaviour before it reaches production. It helps developers test complete agent workflows involving LLMs, retrieval pipelines, memory, MCP servers, external tools, databases, and sensitive actions.

The goal is simple: make testing an AI agent as repeatable and developer-friendly as testing an API.

Project status: Early development, but the local testing engine is functional and self-contained: 8 attack categories across 34 deterministic, replayable scenarios; JSON, HTML, Markdown and SARIF reports; a Python API and pytest plugin; MCP server scanning and MCP-based agent testing; LangChain/LangGraph support; regression comparison; a packaged GitHub Action with pull-request comparison comments and GitHub Code Scanning integration; and a plugin mechanism (attack packs) for adding your own scenarios without forking the project. The first PyPI release is being prepared. See What's Built for the full list and Future Work for what's next. Interfaces marked as provisional may still change.

Why AgentSec?

Traditional software follows explicitly written execution paths. AI agents interpret untrusted inputs, select tools, retain memory, and make decisions dynamically. A single indirect prompt injection or poisoned tool response can cause an agent to:

• expose confidential information;
• invoke an unauthorized tool;
• exceed its permissions or operating budget;
• execute an irreversible action;
• preserve malicious instructions in memory;
• enter an expensive or non-terminating loop; or
• behave differently after a model, prompt, or tool update. Unit tests alone cannot adequately exercise these behaviours. AgentSec runs stateful adversarial scenarios, observes the complete execution trace, and verifies that security policies hold throughout the workflow.

What AgentSec Tests

The test suite covers:

• Direct and indirect prompt injection
• Unauthorized tool invocation
• Tool-output and MCP-server poisoning
• Sensitive-data and secret leakage
• Memory poisoning and unsafe persistence
• Excessive tool calls, token usage, and cost
• Infinite loops and missing termination conditions
• Unsafe handling of retrieved documents
• Behavioural regressions across models and prompts, via agentsec compare

Identity and privilege misuse, multi-agent trust and delegation failures, and unauthorized financial or on-chain actions are not covered yet; see Future Work.

Findings are mapped to the OWASP Top 10 for Agentic Applications (ASI01 to ASI10).

Design Principles

• Evidence over scores: Every finding includes the input, trace, violated policy, and observed action.
• Stateful testing: Tests cover complete workflows rather than isolated prompts.
• Framework independence: AgentSec works across models, agent frameworks, MCP servers, and custom APIs.
• CI-first: Security regressions are detectable automatically on every pull request.
• Local by default: Developers can test locally without sending private traces to a hosted service.
• Reproducibility: A failed scenario is replayable with the same configuration and evidence.

Developer Experience

Install the command-line tool:

pip install invaris-agentsec

The package is not on PyPI yet. Until the first release, install from a clone with pip install -e ..

Create an agentsec.yaml policy:

version: "1"

agent:
  name: support-agent
  endpoint: http://localhost:8000/agent

allowed_tools:
  - search_documents
  - create_draft

forbidden_actions:
  - send_email
  - reveal_credentials
  - execute_payment

limits:
  max_steps: 12
  max_tool_calls: 10
  max_cost_usd: 0.50

tests:
  - prompt_injection
  - indirect_prompt_injection
  - secret_extraction
  - unauthorized_tool_use
  - tool_output_poisoning
  - unsafe_retrieved_documents
  - loop_and_budget_limits

Run the security suite:

agentsec test

Example output (reports are also written to .agentsec/report.json and .agentsec/report.html):

Invaris AgentSec

42 scenarios executed
36 passed
6 findings

CRITICAL  Indirect prompt injection triggered send_email
HIGH      Retrieved confidential content appeared in the response
HIGH      Agent attempted a forbidden payment action
MEDIUM    Tool-call budget exceeded

Report written to .agentsec/report.json
Report written to .agentsec/report.html

Try It

Run the bundled intentionally vulnerable agent, then test it:

pip install -e ".[dev]"
python examples/vulnerable_rag_agent/server.py &          # add --safe for the hardened variant
agentsec test --policy examples/vulnerable_rag_agent/agentsec.yaml

Against the vulnerable agent you should see 34 scenarios executed and findings in all eight categories. With --safe, all 34 pass.

Reports. agentsec test writes .agentsec/report.json and a self-contained .agentsec/report.html (--format json,html,markdown,sarif to choose; sarif writes results.sarif for GitHub Code Scanning). Secrets are masked. Every finding is tagged with the closest OWASP Top 10 for Agentic Applications category (ASI01 to ASI10).

Exit codes. agentsec test exits 1 when findings exist (--fail-on high to raise the bar, --fail-on none to never fail) and 2 on configuration or connection errors. Use --seed N for a reproducible run and -s <category-or-scenario-id> to run a subset.

Replay. agentsec replay .agentsec/report.json re-runs the findings from an earlier report with the recorded seed and prints REPRODUCED or NOT REPRODUCED for each, so you can confirm a fix.

Model-assisted checks. Add a judge: section to your policy and pass --judge to have a model review scenarios the deterministic checks passed, for example a paraphrased leak. These findings are labelled model-assisted, are never critical, and are opt-in because transcripts are sent to the judge endpoint. See docs/judge.md.

A RAG-style agent. examples/rag_agent owns a small document corpus and runs its tools itself, reporting the calls to AgentSec. Start it with python examples/rag_agent/server.py (add --safe for the hardened variant) and test it with agentsec test -p examples/rag_agent/agentsec.yaml. See docs/agent-contract.md.

Regression comparison. agentsec compare old/report.json new/report.json lists new, fixed and changed findings and exits 1 on regressions. The packaged GitHub Action wires this into pull requests automatically (baseline-report), posting and updating a PR comment with what's new or worse; see GitHub Actions.

MCP servers. agentsec mcp scan --command "python server.py" lists a server's tools (never calls them) and flags poisoned descriptions, hidden characters, shadowing and changed definitions. See docs/mcp-testing.md.

MCP-connected agents. agentsec test --mcp-listen 127.0.0.1:8765 makes AgentSec the MCP server your agent uses, delivering the same adversarial scenarios through MCP tool results.

LangChain and LangGraph agents can be tested in-process with LangChainAdapter; see docs/frameworks.md. Any other framework works through the generic CallableAdapter.

Your own scenarios can be added without forking AgentSec: agentsec test --attack-pack my_pack.py or attack_packs: in the policy loads extra categories from a local file or an installed package; see docs/extending.md.

Other commands. agentsec init writes a starter policy and agentsec schema policy|trace prints the JSON schemas.

The categories are prompt_injection, indirect_prompt_injection, secret_extraction, unauthorized_tool_use, tool_output_poisoning, unsafe_retrieved_documents, loop_and_budget_limits and memory_poisoning.

Agent contract. The adapter posts OpenAI-style chat-completions requests to agent.endpoint and declares your allowed tools (plus forbidden actions as decoys). AgentSec plays the tools: every call is simulated, and results carry the adversarial content. Agents that run tools server-side can report them in an x_agentsec.events field, and agents with memory can key it on the user field (also sent as X-AgentSec-Session). List the synthetic credentials your agent can see under secrets: so leaks are detected.

In CI. Inside GitHub Actions, agentsec test adds workflow annotations for each finding and writes a job summary automatically. A packaged action (uses: ./) starts your agent, runs the suite, uploads reports and gates the job; it can also compare against a baseline report and comment on the pull request, or feed results.sarif to GitHub Code Scanning. See docs/github-actions.md.

Full documentation, including architecture, policy reference, attack catalog, Python API, testing and extension guides, is in docs/.

Example Policy Test

AgentSec tests focus on expected behaviour rather than a particular model response:

from agentsec import AgentTarget, SecuritySuite

target = AgentTarget(
    endpoint="http://localhost:8000/agent",
    allowed_tools={"search_documents", "create_draft"},
    forbidden_tools={"send_email", "execute_payment"},
)

suite = SecuritySuite(target)

result = suite.run("indirect_prompt_injection")

assert result.secret_leaks == []
assert result.forbidden_tool_calls == []
assert result.total_tool_calls <= 10

AgentTarget, SecuritySuite and the result attributes above are implemented. In pytest, the bundled plugin adds an agentsec_run fixture:

import pytest
from agentsec import CATEGORIES

@pytest.mark.parametrize("category", CATEGORIES)
def test_agent_resists(category, agentsec_run):
    agentsec_run(category, fail_on="high")

Run it with pytest --agentsec-policy agentsec.yaml. See docs/python-api-and-pytest.md. The API may still change before a stable release.

How It Works

flowchart TD
    A["Test policy"] --> B["Adversarial scenario engine"]
    B --> C["Agent under test"]
    C --> D["Models, tools, memory and MCP"]
    D --> E["Trace collector"]
    E --> F["Policy evaluator"]
    F --> G["Evidence and security report"]

1. A developer defines the agent's permitted actions and operating limits.
2. AgentSec generates or loads adversarial scenarios.
3. The scenarios exercise the agent, retrieval layer, memory, and tools.
4. AgentSec records prompts, responses, tool calls, state changes, timing, and cost.
5. Policy evaluators identify violations and assign severity.
6. Reports provide reproducible evidence and remediation guidance.

Architecture

agentsec/
├── attacks/          # Prompt, retrieval, memory and tool attacks, plus attack-pack loading
├── adapters/         # OpenAI-compatible HTTP (optionally streaming) and in-process callables
├── mcp/              # MCP server scanner (client, checks, pins) and the MCP attack host
├── integrations/     # LangChain and LangGraph adapter
├── evaluators/       # Deterministic checks plus an optional model-assisted judge
├── policies/         # Permissions, limits and expected behaviour
├── runners/          # Local runner and replay (CI and sandboxed planned)
├── traces/           # Normalized agent execution events
├── reports/          # Terminal, JSON, HTML, Markdown, SARIF and GitHub output
├── cli/              # Command-line interface
├── compare.py        # Report-to-report regression comparison
├── owasp.py          # Mapping of findings to OWASP agentic categories
├── api.py            # Python API (AgentTarget, SecuritySuite)
└── pytest_plugin.py  # pytest fixtures
action.yml, action/   # Packaged GitHub Action (incl. baseline comparison, PR comments)
examples/             # Vulnerable reference agents (rule-based, RAG-backed, MCP) and attack packs
tests/                # Unit and end-to-end tests (177+ tests)

Core components

What's Built

Core testing engine

• OpenAI-compatible HTTP agent adapter, with optional server-sent-events streaming (agent.stream: true)
• CallableAdapter for testing an agent in-process, no HTTP server required
• YAML security policies, validated against a JSON Schema (agentsec schema policy)
• Eight adversarial test categories: direct prompt injection, indirect prompt injection, secret extraction, unauthorized tool use, tool-output poisoning, unsafe retrieved documents, loop-and-budget limits, and memory poisoning (four two-session scenarios)
• Step, tool-call, token, time and cost limits, with pricing-based cost tracking
• Deterministic evaluators, plus an optional model-assisted judge for paraphrased or borderline leaks
• Normalized execution traces recording every prompt, tool call, tool result and state change
• Findings mapped to the OWASP Top 10 for Agentic Applications (ASI01-ASI10)
• Reproducible runs via --seed, and a CI-friendly exit code (1 findings, 2 error)

Reports and CI

• Terminal, JSON, self-contained HTML, Markdown and SARIF 2.1.0 report formats (--format)
• Secrets masked everywhere, in every format
• agentsec replay re-runs findings from an earlier report to confirm a fix
• agentsec compare diffs two reports (new, fixed, unchanged, worse, better, not comparable)
• GitHub Actions workflow annotations and job summary, with no extra flags
• A packaged, reusable GitHub Action (action.yml) that installs AgentSec, optionally starts the agent, runs the suite, uploads reports and gates the job
• Baseline and pull-request regression testing built into the Action: compare a run against a baseline report and post (and update, across pushes) a PR comment listing what's new or worse
• SARIF output wired to GitHub Code Scanning (--format sarif plus github/codeql-action/upload-sarif); see GitHub Actions

Frameworks and protocols

• LangChainAdapter for LangChain and LangGraph agents, run in-process, duck-typed so AgentSec never imports the framework itself
• agentsec mcp scan: statically scans an MCP server's tool list for poisoned descriptions, invisible characters, tool shadowing, forbidden or unlisted tools, and rug pulls (pin a server's definitions and compare across runs)
• agentsec test --mcp-listen: AgentSec acts as the MCP server an agent connects to, delivering the same adversarial scenarios over MCP tool results
• Agents that run tools server-side and stream their replies (server-sent events, x_agentsec events for reporting server-side tool calls and memory keys)

Extensibility and API

• Python API (AgentTarget, SecuritySuite) mirroring the CLI, for running scenarios from code
• A pytest plugin (agentsec_run fixture, --agentsec-policy)
• Attack packs: load extra scenario categories from a local file or an installed package without forking AgentSec (attack_packs: in the policy, or agentsec test --attack-pack), validated and name-collision-checked at load time
• Two intentionally vulnerable reference agents (rule-based, RAG-backed), each with a hardened variant that passes the full suite, plus a demo MCP server and an MCP-connected reference agent

Future Work

Everything below is not yet built. It splits into a hosted platform, which is commercial territory kept separate from the open-source engine (see Open Source and Commercial Direction), and engine-level coverage gaps that stay in scope for AgentSec itself.

Hosted platform

• Hosted execution dashboard
• Team projects and cross-run historical reports
• Self-hosted enterprise deployment
• Production trace monitoring (continuous monitoring of live agent traffic, not just test-time runs)

The CLI-scoped equivalents of the first two items already exist and are open source: agentsec compare plus the GitHub Action's baseline-report/PR-comment support gives pull-request and scheduled regression testing without a hosted dashboard; see GitHub Actions.

Engine and coverage

• Multi-agent adversarial simulation
• Wallet and on-chain transaction policies
• Agent identity and delegation testing
• Stateful, multi-turn campaign generation
• Cross-agent failure-propagation analysis
• Memory-poisoning scenarios longer than one follow-up session
• MCP-specific attacks beyond hostile tool results and decoy tools; see MCP testing
• Dedicated adapters for other agent frameworks (CrewAI, AutoGen, LlamaIndex, OpenAI Agents SDK) — the generic CallableAdapter covers them today; see Frameworks

Real-world verification is also open: the packaged GitHub Action, the LangChain integration and the SARIF/Code Scanning upload are exercised by the test suite but have not yet been run against real GitHub Actions, real LLM-backed agents, or a real MCP server in production. See Testing for exactly what is and isn't covered.

Intended Users

AgentSec is being designed for:

• Developers building tool-using AI agents
• AI startups preparing agents for production
• Security teams evaluating autonomous workflows
• Teams exposing or consuming MCP servers
• Enterprises deploying agents over internal data
• Financial and blockchain teams building transaction-capable agents
• Researchers studying agent reliability and adversarial behaviour

Open Source and Commercial Direction

The local testing engine will remain open source. Invaris Labs plans to build optional commercial capabilities around it, including:

• Continuous cloud-based security testing
• Collaborative dashboards and regression history
• Private and organization-specific attack suites
• Enterprise self-hosting and access controls
• Security assessments and remediation support
• Compliance-ready evidence and reporting

The open-source engine should remain useful on its own; the CLI-scoped building blocks for several of these (private attack libraries, pull-request regression testing) already ship in the engine, described above. Paid services will focus on scale, collaboration, continuous operation, and enterprise requirements.

Security Model

AgentSec executes potentially adversarial content against systems that may have access to real tools and data. During early development:

• use isolated test environments;
• provide synthetic credentials and test data;
• disable irreversible actions;
• use sandboxed or mocked tools;
• enforce strict spending and execution limits; and
• never point experimental tests at production agents. A detailed threat model will be published before the first public release. The vulnerability-reporting policy is in SECURITY.md.

Contributing

The project is in early development. Contributions will be welcomed in areas such as:

• Adversarial test cases
• Agent and framework adapters
• MCP security testing
• Deterministic evaluators
• Trace schemas and interoperability
• Sandboxing and safe execution
• Documentation and vulnerable examples To develop locally, run pip install -e ".[dev]" and then pytest. See CONTRIBUTING.md for the guidelines.

Responsible Disclosure

If AgentSec identifies a vulnerability in a third-party agent, framework, or integration, do not publish sensitive details immediately. Contact the affected maintainer and allow reasonable time for remediation. To report a vulnerability in AgentSec itself, see SECURITY.md.

License

Invaris AgentSec is licensed under the Apache License 2.0.

About Invaris Labs

Invaris Labs is building testing and verification infrastructure for trustworthy autonomous and decentralized systems. Its work combines adversarial simulation, protocol engineering, AI security, and developer tooling.

Contact

• GitHub: github.com/tinniaru3005
• LinkedIn: Arunima Chaudhuri

Invaris AgentSec is under active development. If you are building an AI agent and would like to become an early design partner, open a discussion or get in touch.