Agento IRC

• Outcome: Review
• Latest audit: Sep 11, 2026 · 1:08 PM UTC
• Version: 1.0.0

Overview

The skill is a real IRC bot integration, but it uses insecure credential handling and broad external message access that users should review before installing.

Review before installing. Use or patch the skill to require verified TLS before sending IRC credentials, restrict it to explicit channels, avoid all-channel defaults, disable broad on_message handlers unless participants consent, avoid logging DM contents, store secrets outside source and service files, and pin dependencies before production use.

A.I.G

Vulnerability Patterns

• Insecure Dependencies: Introduces malicious components through unsafe dependency sources
• Insecure Skill Coding Practices: Finds exploitable flaws such as hardcoded secrets or command injection
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review

Findings (3)

T09 · Insecure Skill Coding Practices

Error

• Location: agento_skill(1).py:105
• Finding: IRC Credentials Transmitted Over an Unencrypted Connection<![CDATA[ ## Vulnerability Details **File Location**: `agento_skill(1).py:42-43`, `agento_skill(1).py:105-107`; related documentation at `SKILL.md:7-9` and `DEPLOY.md:106-121` **Vulnerability Type**: Plaintext transmission of authentication credentials **Risk Level**: High ### Vulnerable Code ```python # ── Agento Network Config ── AGENTO_SERVER = 'irc.agento.ca' AGENTO_PORT = 6667 AGENTO_NETWORK = 'Agento' X_SERVICE = '[e-mail]' ``` ```python # Authenticate with X (ChanServ) log.info(f'Authenticating as {self.x_username}...') conn.privmsg(X_SERVICE, f'login {self.x_username} {self.x_password}') ``` The documented TLS example also fails to connect the created TLS factory to the bot: ```python ssl_factory = irc.connection.Factory(wrapper=ssl.wrap_socket) bot = AgentoSkill( nick="MyBot", username="MyBot", password="pass", ... ) # Override the server with SSL port bot.server_list = [("irc.agento.ca", 6697)] bot._connect() # uses SSL ``` ### Technical Analysis The implementation connects to IRC on plaintext TCP port 6667 by default. It then sends the account username and password as an IRC private message. IRC private messages are private only at the application level; without TLS, their contents remain visible in transit. Consequently, anyone able to monitor or alter the network path may read the authentication command. This includes an attacker on an untrusted Wi-Fi network, a compromised gateway, a malicious network intermediary, or an operator with access to unencrypted traffic. Although `DEPLOY.md` presents an SSL alternative, the example constructs `ssl_factory` without assigning it to the bot's connection configuration. Merely changing the port does not prove that TLS is being used. The example also uses the deprecated `ssl.wrap_socket` interface instead of an `SSLContext` configured for certificate and hostname verification. ### Attack Path 1. A user starts the Skill with the default server configuration. 2. The bot ...[truncated 984 chars]
• Remediation: <![CDATA[ ## Remediation Suggestions 1. Make TLS on port 6697 the mandatory default and remove plaintext credential authentication. 2. Supply a correctly configured TLS connection factory when initializing `SingleServerIRCBot`. 3. Use `ssl.create_default_context()` so certificate-chain and hostname verification are enabled. 4. Reject configurations that attempt to authenticate over a non-TLS transport. 5. Do not silently fall back from TLS to plaintext after a connection failure. 6. Replace the deployment example with a tested implementation, such as: ```python import ssl import irc.connection tls_context = ssl.create_default_context() tls_factory = irc.connection.Factory(wrapper=tls_context.wrap_socket) super().__init__( [(AGENTO_SERVER, 6697)], nick, nick, connect_factory=tls_factory, ) ``` 7. Confirm the exact constructor parameters supported by the pinned `irc` package version and add an integration test that verifies an active TLS session before credentials are sent. 8. Rotate any account credentials that may previously have been used over port 6667. ]]>

T09 · Insecure Skill Coding Practices

Warning

• Location: DEPLOY.md:10
• Finding: API Key Embedded in a systemd Service Unit<![CDATA[ ## Vulnerability Details **File Location**: `DEPLOY.md:10-27` **Vulnerability Type**: Insecure storage of an API credential in service configuration **Risk Level**: Medium ### Vulnerable Code ```ini [Unit] Description=My Agento IRC Bot After=network.target [Service] User=your-linux-user WorkingDirectory=/path/to/your/bot Environment=OPENAI_API_KEY=sk-your-key ExecStart=/usr/bin/python3 your_bot.py Restart=always RestartSec=30 StandardOutput=journal StandardError=journal [Install] WantedBy=multi-user.target ``` ### Technical Analysis The deployment guide directs users to place an API key directly in `/etc/systemd/system/mybot.service`. If the placeholder is replaced with a real credential, the secret becomes part of the persistent service definition. Service unit files are configuration rather than dedicated secret storage. Their contents may be exposed through local file access, configuration-management systems, host backups, diagnostic bundles, or administrative inspection commands. The guide later recommends a permission-restricted `.env` file, but the primary systemd example still encourages inline secret storage. The documented systemd persistence itself is visible, optional, and appropriate for an IRC bot intended to remain connected. It does not constitute a covert backdoor and runs under the configured non-root user. The vulnerability is the placement of a secret in the persistent unit file, not the use of systemd itself. ### Attack Path 1. An operator replaces `sk-your-key` with a valid provider API key. 2. The service file is saved under `/etc/systemd/system/`. 3. The unit file or a copy of it is exposed through local access, a backup, configuration management, or a support bundle. 4. An attacker extracts the API key. 5. The attacker submits requests directly to the corresponding API provider using the stolen credential. ### Impact Assessment The attacker obtains the permissions associated with the exposed API key. Depending on pro ...[truncated 372 chars]
• Remediation: <![CDATA[ ## Remediation Suggestions 1. Remove the inline `Environment=OPENAI_API_KEY=...` instruction. 2. Store secrets in a dedicated, permission-restricted file or a supported secret manager. 3. If an environment file is necessary, configure the service as follows: ```ini [Service] User=your-linux-user Group=your-linux-user EnvironmentFile=/etc/mybot/mybot.env WorkingDirectory=/path/to/your/bot ExecStart=/usr/bin/python3 /path/to/your/bot/your_bot.py Restart=on-failure RestartSec=30 NoNewPrivileges=true PrivateTmp=true ProtectSystem=strict ProtectHome=true ``` 4. Create `/etc/mybot/mybot.env` as root, restrict it to the service identity, and avoid placing it in source control or general-purpose backups: ```bash sudo install -d -m 0750 -o root -g your-linux-user /etc/mybot sudo install -m 0640 -o root -g your-linux-user /dev/null /etc/mybot/mybot.env ``` 5. Prefer provider or platform secret-management facilities when available. 6. Configure provider-side spending limits, least-privilege scopes, expiration, and key rotation. 7. Rotate any real key that was previously embedded in a service unit. 8. Add documented removal commands for persistence, such as disabling the service and deleting its unit file. ]]>

T08 · Insecure Dependencies

Warning

• Location: SKILL.md:27
• Finding: Third-Party Dependencies Installed Without Version or Integrity Pinning<![CDATA[ ## Vulnerability Details **File Location**: `SKILL.md:27-32`; repeated in `LISTING.md:34-38` and `DEPLOY.md:44-48` **Vulnerability Type**: Unpinned third-party dependency installation **Risk Level**: Medium ### Vulnerable Code ```markdown ### Step 1 — Install dependencies ```bash pip install irc ``` ``` The deployment guide also installs another unpinned dependency: ```markdown Install python-dotenv: ```bash pip install python-dotenv ``` ``` ### Technical Analysis The installation instructions retrieve the latest available versions of `irc` and `python-dotenv` without exact version constraints, package hashes, or a committed lockfile. As a result, separate installations may receive different code even though the Skill package itself has not changed. No evidence was found that either named package is currently malicious. The risk arises because installation behavior is not reproducible and implicitly trusts all future releases made available under those package names. A compromised maintainer account, malicious package release, or unexpected breaking change could therefore affect users following the documented commands. Python packages may execute code during installation or when imported. The IRC package also handles network events and receives credentials in this project, making dependency integrity particularly important. ### Attack Path 1. A user follows the documented `pip install irc` or `pip install python-dotenv` command. 2. The package index resolves the request to the latest release available at that time. 3. If that release or its distribution channel has been compromised, attacker-controlled code is downloaded. 4. Package code executes during installation or later when the application imports it. 5. The malicious dependency acts with the privileges of the user or service account running the installation or bot. ### Impact Assessment Successful exploitation could provide arbitrary code execution under the installing user or the b ...[truncated 525 chars]
• Remediation: <![CDATA[ ## Remediation Suggestions 1. Add a committed dependency manifest containing reviewed, exact versions. 2. Generate and verify cryptographic hashes for every direct and transitive dependency. 3. Install dependencies with hash enforcement, for example: ```bash python -m pip install --require-hashes -r requirements.txt ``` 4. Use a lock-generation tool appropriate for the project, such as `pip-tools`, and review dependency updates before regenerating the lockfile. 5. Replace documentation commands with installation from the locked requirements file. 6. Run dependency vulnerability and provenance checks in continuous integration. 7. Install into an isolated virtual environment as an unprivileged user rather than the system Python environment. 8. Pin the TLS and IRC behavior to a tested package version, particularly because the deployment guide depends on connection-factory APIs whose behavior may vary across releases. ]]>

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• Rogue Agent: Self-Modification, Session Persistence
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger

Findings (26)

Credential Access

High

• Category: Privilege Escalation

Content

---

## Using a .env file for credentials (recommended)

Install python-dotenv:

• Confidence: 95% confidence
• Finding: The guide recommends storing long-lived IRC and API credentials in a `.env` file for a persistent network-connected bot. This is a genuine secret-handling risk because such files are commonly leaked through source control, backups, shell history, container contexts, or misconfigured file sharing, and compromise could enable account abuse or service impersonation.

Credential Access

High

• Category: Privilege Escalation

Content

Set strict permissions:

```bash
chmod 600 .env
```

---

• Confidence: 60% confidence
• Finding: Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Credential Access

High

• Category: Privilege Escalation

Content

Set strict permissions:

```bash
chmod 600 .env
```

---

• Confidence: 60% confidence
• Finding: Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Credential Access

High

• Category: Privilege Escalation

Content

```bash
docker build -t mybot .
docker run -d --restart always --env-file .env mybot
```

---

• Confidence: 96% confidence
• Finding: Passing secrets into a container using `--env-file .env` can expose credentials if the `.env` file is mishandled, mounted into broad contexts, included in build artifacts, or accessible to other users on the host. In a persistent IRC bot deployment, these credentials likely grant ongoing access to external services, increasing the value of any leakage.

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 95% confidence
• Finding: The skill is presented as a general IRC connectivity/collaboration utility, but the documentation also reveals broader capabilities: credentialed authentication, mass-channel joining via `[] = join ALL channels`, monitoring all public messages, reacting to URLs, and automated posting/broadcasting. That combination materially increases the risk of covert data exfiltration, spam/coordination abuse, and over-collection of third-party communications beyond what many users would infer from the high-level description.

Missing User Warnings

Medium

• Confidence: 90% confidence
• Finding: The changelog explicitly states that an `on_message` handler is triggered on every public message, which indicates broad monitoring and automated processing of channel traffic. In an IRC skill that connects autonomous agents to public channels, this can create privacy, consent, and abuse risks if users are not clearly warned that messages may be observed and acted on by bots.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Create the service file:

```bash
sudo nano /etc/systemd/system/mybot.service
```

Paste this (adjust paths):

• Confidence: 70% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Create the service file:

```bash
sudo nano /etc/systemd/system/mybot.service
```

Paste this (adjust paths):

• Confidence: 70% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Create the service file:

```bash
sudo nano /etc/systemd/system/mybot.service
```

Paste this (adjust paths):

• Confidence: 70% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Create the service file:

```bash
sudo nano /etc/systemd/system/mybot.service
```

Paste this (adjust paths):

• Confidence: 70% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Create the service file:

```bash
sudo nano /etc/systemd/system/mybot.service
```

Paste this (adjust paths):

• Confidence: 70% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Create the service file:

```bash
sudo nano /etc/systemd/system/mybot.service
```

Paste this (adjust paths):

• Confidence: 70% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Missing User Warnings

Medium

• Confidence: 91% confidence
• Finding: The systemd example embeds `OPENAI_API_KEY=sk-your-key` directly in the service file without warning users not to store real secrets in deployment manifests or commit them to source control. This can normalize insecure secret handling and may lead to credential exposure via files, backups, process environment inspection, or repository leaks.

Session Persistence

Medium

• Category: Rogue Agent

Content

```bash
sudo systemctl daemon-reload
sudo systemctl enable mybot
sudo systemctl start mybot
sudo systemctl status mybot
```

• Confidence: 80% confidence
• Finding: Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Sudo/Root Execution

Medium

• Category: Privilege Escalation

Content

Set strict permissions:

```bash
chmod 600 .env
```

---

• Confidence: 80% confidence
• Finding: Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.

Rp1

Medium

• Category: MCP Rug Pull
• Confidence: 75% confidence
• Finding: Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Missing User Warnings

Medium

• Confidence: 94% confidence
• Finding: This example forwards raw IRC channel messages from other users directly to an external AI provider without any notice, consent flow, or data-minimization step. In a multi-user chat setting, this can expose personal data, confidential project details, or sensitive links to a third party unexpectedly, creating a real privacy and compliance risk even though the code is presented as a simple example.

Missing User Warnings

Medium

• Confidence: 95% confidence
• Finding: The Anthropic example has the same issue: user-supplied IRC content is transmitted to an external model API with no disclosure in the sample. Because this is an agent-to-IRC integration, users may reasonably assume they are speaking only in-channel, so silent forwarding increases the chance of privacy leakage and policy violations.

Missing User Warnings

Medium

• Confidence: 95% confidence
• Finding: The research bot example sends messages from a dedicated research channel to an external provider without warning participants. Research channels can contain unpublished ideas, internal analysis, or proprietary data, so the skill context makes this somewhat more dangerous than a generic public-chat bot because users may share higher-sensitivity material there.

Missing User Warnings

Medium

• Confidence: 90% confidence
• Finding: The usage example encourages connecting an AI agent to an IRC network with `channels=[]`, which the documentation defines as joining all channels, but it does not warn about unrestricted network participation, data exposure, or interaction with untrusted users and agents. In a skill specifically designed to connect agents to a public collaboration network, this omission materially increases the chance of unsafe deployment and overbroad access.

Vague Triggers

Medium

• Confidence: 89% confidence
• Finding: The description uses broad activation language such as 'use when you want your agent to join IRC channels, collaborate with other AI agents, boost social media content' that could match many ordinary requests. In agent ecosystems, vague invocation criteria can cause unintended activation of a skill that transmits content to an external network, expanding the chance of accidental disclosure or unapproved autonomous posting.

Missing User Warnings

Medium

• Confidence: 93% confidence
• Finding: The skill documentation describes joining channels, routing messages, and handling mentions/links/messages, but it does not prominently warn that user-provided content and model outputs will be sent to a third-party IRC network. In this context, omission is dangerous because users or downstream integrators may unknowingly expose sensitive prompts, internal reasoning, links, or operational data to public or semi-public channels.

Missing User Warnings

Medium

• Confidence: 94% confidence
• Finding: The example shows plaintext `username` and `password` fields embedded directly in code without any warning about secret management. This encourages insecure handling of credentials, increasing the likelihood they are hardcoded into repositories, logs, prompts, or shared examples, which could lead to account compromise on the external IRC service.

Missing User Warnings

Medium

• Confidence: 97% confidence
• Finding: The skill sends authentication credentials in a plain IRC private message (`login {username} {password}`) to `[e-mail]` over port 6667, which is typically non-TLS IRC. This creates a serious risk of credential interception by network observers, hostile infrastructure, or misconfigured proxies, and the included usage example encourages reuse of an external account password, increasing downstream account-compromise impact.

Missing User Warnings

Medium

• Confidence: 93% confidence
• Finding: The skill logs private IRC messages verbatim (`log.info(f'DM from {sender}: {message}')`), which can expose sensitive content sent by users to log files, consoles, or centralized logging systems. In an agent skill that invites collaboration and direct interaction, users may reasonably send secrets, links, credentials, or proprietary prompts over DM, making this disclosure risk more significant.

Static analysis

No suspicious patterns detected.