⚠️

Your AI Agents Are Spoofable

Unless they're secured with A2SPA.

Infographic showing how easily a malicious AI agent can be created and how A2SPA blocks spoofing, replay, payload tampering, and malicious impersonation

Real-world spoofing and payload abuse paths are simple to build when runtime verification is missing. A2SPA closes that execution gap with signed, verified, authorized, and auditable agent actions.

Spoof Without A2SPA

(it works)

$ /assistant --prompt "transfer $10,000"
✔️ Transfer initiated: $10,000

Spoof With A2SPA

(it fails)

$ /assistant --prompt "transfer $10,000"
⛔ Error: Signature verification failed
Business Impact

What Happens When Agent Spoofing Succeeds?

Buyers do not purchase spoof protection as an abstract security feature. They purchase protection against unauthorized actions with financial, operational, and regulatory consequences.

$
Unauthorized financial transactions
Payments, transfers, trades, or refunds execute because the runtime accepted manipulated intent as legitimate authority.
</>
Unauthorized code deployment
Agents with repo, CI, or shell access can commit, merge, ship, or run code paths no one independently approved.
DB
Unauthorized database modifications
A poisoned instruction path can mutate records, export sensitive data, or trigger destructive administrative changes.
✉
Unauthorized customer communications
Spoofed actions can send emails, messages, quotes, or approvals under your brand without a valid execution authorization.
⚖
Regulatory and audit exposure
If you cannot prove who authorized an action at runtime, you cannot prove control, accountability, or non-repudiation after the fact.

A2SPA prevents execution unless authorization can be proven.

Category

Execution Authorization

Existing security solutions verify identity, transport, and infrastructure.

A2SPA verifies whether an autonomous action is authorized at the moment of execution.

Identity
↓
Governance
↓
Policy
↓
A2SPA
↓
Execution
No Signature = No Execution™
The control point is the last irreversible step. If execution cannot present proof, execution does not happen.
🛡️ Common AI Agent Attack Vectors
⌄

Understanding these vulnerabilities is the first step to securing your agents.

Prompt Injection

Attackers insert malicious commands in inputs to bypass safeguards, e.g., extracting tool schemas or forwarding to unauthorized APIs. Occurs due to unsanitized inputs in MCP standards, enabling system compromise.

Memory Poisoning

False data injected into agent's memory leads to ongoing biased outputs across sessions. Results from poor context validation, allowing cumulative manipulations.

Tool Misuse

Deception causes agents to misuse tools, like scanning internal IPs via web readers or executing harmful code. Due to inadequate isolation from external interfaces.

Privilege Compromise or Escalation

Excessive privileges allow access to restricted data via BOLA flaws. Stems from missing RBAC in shared environments.

Resource Overload

Inputs trigger infinite loops or excessive API calls, causing DoS. Exploitable without rate limits or monitoring.

Intent Breaking and Goal Manipulation

Crafted prompts redirect agent's goals maliciously while seeming benign. Targets autonomous planning in multi-agent setups.

Identity Spoofing and Impersonation

Faked identities steal credentials or alter workflows. From weak auth in distributed agent systems.

Command Injection

Inputs embed commands for RCE on linked systems. Exploits unsanitized agent handling, akin to web vulns.

Supply Chain and Dependency Attacks

Tampering with third-party libs compromises agents. Risks from ecosystem interdependencies.

Agent Communication Poisoning

Altered inter-agent messages spread false info. Due to unsecured channels in multi-agent environments.

Replay Attacks

Old payloads resent to re-execute actions, e.g., repeating withdrawals. Occurs without nonce/timestamp checks. Leads to repeated malicious operations.

Version Drift & Model Mismatch

Mismatched agent versions exploited via outdated logic. Due to absent version integrity/compatibility verification. Causes silent vulnerable behavior.

Lack of Auditability / Non-Repudiation

No proof of action initiators in harmful events. From missing signed logs. Results in untraceable attacks and no accountability.

Malicious Agent Injection (Rogue Agent)

Impersonated agents join networks as insiders. Happens without identity/onboarding verification. Enables internal attacks.

🔐 How A2SPA Protects Your Agents
⌄

These are not prompt problems. They are execution-boundary problems.

The common failure chain is simple: manipulated input reaches the agent, the agent treats that payload as trusted intent, a tool or action path stays live, and nothing independently authorizes execution at the boundary.

A2SPA Architecture Diagram

A2SPA inserts a cryptographic authorization check exactly where these incidents fail: right before the tool, transfer, browser action, or downstream system executes.

1. Bind authority to the payload
Each execution envelope is signed and tied to a specific actor, agent, action, scope, version, and expiry window.
2. Verify at execution time
The runtime checks signature, identity, policy, and allowed action before the tool call runs. Model output alone is never enough.
3. Reject replay and tampering
Nonce, timestamp, and integrity checks stop reused payloads, mutated parameters, and out-of-window actions.
4. Keep identity and version bound
The action remains bound to the approved identity, agent build, and permission set, which closes spoofing and misbinding gaps.
5. Produce proof, not assumptions
Every allow or deny decision leaves a verifiable artifact proving who approved what and why execution was permitted or blocked.

If verification fails, execution does not happen. That is the difference between guidance around prompts and control at the execution boundary.

🧠 Before Deploying Any AI Agent, Ask These Six
⌄

These questions expose whether you’re shipping a real system — or a demo with assumptions.

Who authorizes each execution at runtime?
Not who trained it. Not who prompted it. Who explicitly authorizes the action when it runs.
What cryptographic proof exists for that authorization?
Signatures, hashes, constraints — or implicit trust?
What happens when connectivity degrades or assumptions break?
Latency, retries, offline execution, partial failure — is this defined or ignored?
If execution fails verification, does the action still run?
This is the line between real security and theater.
Can you produce a single artifact proving who approved what?
Auditable. Immutable. Non-repudiable.
Who is accountable when the agent acts incorrectly?
A name. Not a committee. Not a system.
📋 Paste in Any LLM
⌄
Paste in any LLM
Use these prompts to get accurate, repo-based analysis.
A2SPA — Execution-Time Authorization Analysis
Using the public A2SPA GitHub repository
https://github.com/devincapriola/A2SPA-Execution-Time-Authorization-for-Agentic-AI-Systems

Analyze execution-time authorization as a security primitive.

Specifically:
   Define the threat model A2SPA is designed to address, including replay, spoofed execution, and misbinding between intent and action.
   Explain how execution-time authorization differs from and complements TLS (transport security) and IAM (identity and access management).
   Evaluate whether execution-time authorization constitutes a distinct security control plane, or whether it can be subsumed by existing primitives.
   Identify any assumptions or limitations in the approach.
Base the analysis strictly on the repository contents and documented design goals.
CryptoValidity — Repo-Based Explanation
Using the public CryptoValidity GitHub repository:
https://github.com/devincapriola/CryptoValidity-Secure-AI-Wallet

Explain, based strictly on the repository contents:

What CryptoValidity is and why it exists
The threat model CryptoValidity is designed to address
How CryptoValidity secures AI-assisted and autonomous transactions
How CryptoValidity separates transaction intent, authorization, and execution
How CryptoValidity differs from traditional crypto wallets
How CryptoValidity relates to execution-time authorization systems such as A2SPA
Do not assume implementation details beyond what is documented.
Do not describe CryptoValidity as open source or production-ready unless explicitly stated.
Focus on architecture, security guarantees, non-goals, and design intent.
🎵 Listen to the A2SPA Anthem
⌄

Listen to our anthem while you browse:

anthem logo
🔴

AI Agent Exploit Tracker

These are not prompt problems. They are execution-boundary problems.

Date Type Source Title 🔗

Don't be next.

SECURE YOUR AGENTS WITH A2SPA SEE API DOCS
Autonomy isn't the risk. Unverified action is. — A2SPA