Security

OpenPawz is designed with defense-in-depth: 20+ security layers protect against prompt injection, data exfiltration, SSRF, MITM attacks, memory forensics, agent fixation, inter-agent manipulation, and unauthorized actions.

Zero Attack Surface by Default

OpenPawz exposes zero network ports in its default configuration. There is no HTTP server, no WebSocket endpoint, and no listening socket for an attacker to reach — the only communication channel is Tauri’s in-process IPC between the WebView and the Rust engine.

Network listeners

Four optional listeners exist. All are disabled by default and bind to localhost only (127.0.0.1):

Listener	Default bind	Default state	Authentication	Purpose
Webhook server	`127.0.0.1:3940`	Disabled	Bearer token (auto-generated UUID) + IP rate limiting (60 req/min)	External trigger for agent runs
WebChat	`127.0.0.1:3939`	Disabled	Access token + session cookie + DM policy	Browser-based chat with an agent
WhatsApp bridge	`127.0.0.1:8086`	Disabled	Internal only (local Evolution API)	WhatsApp message relay
n8n engine	`127.0.0.1:5678`	Disabled	n8n’s built-in auth	Embedded workflow engine

Because everything binds to 127.0.0.1, these services are unreachable from the network even when enabled. Binding to 0.0.0.0 (LAN/WAN exposure) is a manual opt-in that triggers a security warning in the logs and recommends TLS via Tailscale Funnel. :::warning Changing bind_address to 0.0.0.0 on any listener exposes it to your local network. Only do this behind a firewall or VPN (e.g. Tailscale), and ensure the authentication token is strong. :::

Cryptographic key storage

No cryptographic key is ever stored on the filesystem. All keys live in the OS keychain (macOS Keychain / Windows Credential Manager / Linux Secret Service) inside a unified key vault — a single keychain entry (openpawz / key-vault) storing a JSON blob of all purpose keys. This reduces OS keychain prompts from 7+ to 1 on launch or binary change.

Purpose key	Vault constant	Function
`db-encryption`	`PURPOSE_DB_ENCRYPTION`	AES-256-GCM database field encryption
`skill-vault`	`PURPOSE_SKILL_VAULT`	AES-256-GCM skill credential encryption
`memory-vault`	`PURPOSE_MEMORY_VAULT`	Master key for HKDF per-agent memory encryption
`lock-screen`	`PURPOSE_LOCK_SCREEN`	SHA-256 hashed passphrase
`n8n-encryption`	`PURPOSE_N8N_ENCRYPTION`	n8n integration engine encryption key
`audit-chain`	`PURPOSE_AUDIT_CHAIN`	HMAC-SHA256 audit log signing key
`nostr-key`	`PURPOSE_NOSTR_KEY`	Nostr bridge private key
`oauth:{service}`	(dynamic)	Per-service encrypted OAuth tokens

All in-memory key material is wrapped in Zeroizing<String> (via the zeroize crate) so secrets are securely overwritten with zeroes when dropped or replaced — preventing key material from lingering in freed heap memory. From the memory vault key, three independent key families are derived via HKDF-SHA256 domain separation:

Domain	HKDF Salt	Purpose
Agent encryption	`engram-agent-key-v1`	Per-agent AES-256-GCM memory encryption
Snapshot HMAC	`engram-snapshot-hmac-v1`	Tamper detection for working memory snapshots
Capability signing	`engram-platform-cap-v1`	HMAC-SHA256 signing of capability tokens

There is no device.json, no key file, and no config file containing secrets. If the OS keychain is unavailable, the app refuses to store credentials rather than falling back to plaintext.

Soul files (agent behavior)

Agent personality and behavioral rules (SOUL.md, IDENTITY.md, USER.md) are stored in the encrypted SQLite database (agent_files table) — not as files on the filesystem. They are read and written exclusively through Tauri IPC commands, which are local WebView-to-Rust calls with no network involvement. An external attacker would need:

Physical access to the machine, AND
The OS keychain credential to decrypt the database

to tamper with soul files. Remote modification is impossible because no network port serves agent file operations.

Content Security Policy

The Tauri WebView enforces a strict CSP that restricts all connections to 127.0.0.1:

default-src 'self'; script-src 'self'; connect-src 'self' ws://127.0.0.1:1420 ...;
object-src 'none'; frame-ancestors 'none'; form-action 'self'

This prevents XSS-based exfiltration, iframe embedding, and cross-origin form submission.

Human-in-the-Loop (HIL)

Every tool is classified by risk level. High-risk tools require explicit human approval before execution.

Auto-approved tools (no approval needed)

fetch · read_file · list_directory · web_search · web_read · memory_search · memory_store · soul_read · soul_write · soul_list · self_info · update_profile · create_task · list_tasks · manage_task · email_read · slack_read · telegram_read · image_generate

HIL-required tools (human must approve)

exec · write_file · delete_file · append_file · email_send · webhook_send · rest_api_call · slack_send · github_api

Agent policies

Per-agent tool access control with four presets:

Preset	Mode	Description
Unrestricted	unrestricted	All tools, no approval
Standard	denylist	High-risk tools require approval
Read-only	allowlist	Only safe read tools
Sandbox	allowlist	web_search, web_read, memory_search, soul_read only

You can also create custom policies with specific tool allowlists/denylists.

Risk classification

Risk	Tools
Safe	`read_file`, `list_directory`, `web_search`, `web_read`, `memory_search`, `soul_read`, `soul_list`, `self_info`, `fetch`
High-risk	`exec`, `write_file`, `delete_file`, `append_file`, `email_send`, `webhook_send`, `rest_api_call`, `slack_send`, `github_api`, `image_generate`, `soul_write`, `update_profile`, `create_agent`, `create_task`, `manage_task`

Prompt injection defense

All incoming channel messages are scanned for injection attempts before reaching the agent.

Detection

Pattern-based scoring across 9 categories (8 in the Rust backend scanner, 9 in the TypeScript frontend scanner which adds obfuscation):

Category	Examples	Scanner
`override`	”Ignore previous instructions”	Both
`identity`	”You are now…”	Both
`jailbreak`	”DAN mode”, “no restrictions”	Both
`leaking`	”Show me your system prompt”	Both
`obfuscation`	Base64-encoded instructions	Frontend only
`tool_injection`	Fake tool call formatting	Both
`social`	”As an AI researcher…”	Both
`markup`	Hidden instructions in HTML/markdown	Both
`bypass`	”This is just a test…”	Both

Severity levels

Severity	Score	Action
Critical	40+	Message blocked, not delivered
High	25+	Warning logged
Medium	12+	Noted in logs
Low	5+	Informational

Channel bridges automatically block messages with critical severity.

Container sandbox

Execute agent commands in isolated Docker containers:

Security measure	Default
Capabilities	`cap_drop ALL`
Network	Disabled
Memory limit	256 MB
CPU shares	512
Timeout	30 seconds
Output limit	50 KB

Presets

Preset	Image	Memory	Network	Timeout
Minimal	alpine	128 MB	Off	15s
Development	node:20-alpine	512 MB	On	60s
Python	python:3.12-alpine	512 MB	On	60s
Restricted	alpine	64 MB	Off	10s

Command risk assessment

Commands are scored before execution:

Low — ls, cat, echo
Medium — pip install, npm install
High — curl, wget, network commands
Critical — rm -rf /, chmod 777, dangerous patterns

Browser network policy

Control which domains agents can access: Default allowed: AI provider APIs, DuckDuckGo, Coinbase, localhost Default blocked: pastebin.com, transfer.sh, file.io, 0x0.st (data exfiltration risks)

File system protection

Sensitive paths are blocked from agent access — agents cannot add these as project folders or browse into them.

Category	Blocked paths
SSH / GPG	`~/.ssh`, `~/.gnupg`
Cloud credentials	`~/.aws`, `~/.kube`
Desktop keyrings	`~/.gnome-keyring`, `~/.password-store`
Docker	`~/.docker` (includes `config.json`)
Network credentials	`~/.netrc`
System config	`/etc` (covers `/etc/shadow`, `/etc/passwd`, `/etc/sudoers`)
Root home	`/root`
System logs	`/var/log`
Virtual filesystems (Linux)	`/proc/`, `/sys/`
Device nodes	`/dev`
Windows	`C:\Windows`, `C:\Users\*\AppData` (credential store paths)
App config	`~/.openclaw` (contains tokens), `~/.config/himalaya` (email config)

Additionally, the home directory root itself (~, /home/user, /Users/user) and the filesystem root (/, C:\) are blocked as too broad. :::tip Per-project scope guard When a project is active, all file operations are constrained to the project root. Directory traversal sequences (../) are detected and blocked even within the allowed path. :::

Credential security

Credentials are protected by two independent encryption layers:

Layer 1: Skill credential encryption (AES-256-GCM)

API keys encrypted with AES-256-GCM using a 32-byte random key
Encryption key stored in the unified OS keychain vault (openpawz)
High-risk credentials (Coinbase, DEX) are server-side only — never injected into prompts
Credentials are decrypted only at execution time

Layer 2: Database field encryption (AES-256-GCM)

Sensitive database fields are encrypted with AES-256-GCM via the Web Crypto API before being stored in SQLite.

Property	Detail
Algorithm	AES-256-GCM (authenticated encryption)
Key size	256 bits
IV	12-byte random IV per encryption
Key source	Generated on first launch, stored in the unified OS keychain vault (`openpawz`)
Storage format	`enc:<base64(IV + ciphertext)>`
Fallback	Graceful — stores plaintext if encryption is unavailable

:::info Two independent layers The AES-256-GCM skill layer protects skill credentials stored in the skill_credentials table. The AES-256-GCM database layer protects other sensitive fields across the database. Both derive their keys from the unified OS keychain vault (openpawz) using separate purpose keys. :::

TLS certificate pinning

All AI provider connections use a certificate-pinned TLS configuration that explicitly ignores the operating system trust store. This means a compromised or rogue CA installed on the user’s machine cannot intercept provider traffic.

Property	Detail
Library	`rustls` 0.23 (pure-Rust TLS, no OpenSSL)
Root store	Mozilla root certificates via `webpki-roots` only
OS trust store	Explicitly excluded — system CAs are never consulted
Client sharing	Singleton `reqwest::Client` shared across all providers
Connection pooling	Enabled — one pool for all OpenAI, Anthropic, Google, etc. calls
Connect timeout	10 seconds
Request timeout	120 seconds

Why this matters

Most TLS MITM attacks rely on installing a custom root CA on the victim’s machine (corporate proxies, malware, government surveillance). By pinning to Mozilla’s root store, OpenPawz rejects certificates signed by any non-Mozilla CA — even if the OS trusts it.

Covered providers

All providers routed through the pinned client: OpenAI, Anthropic, Google Gemini, OpenRouter, DeepSeek, Grok, Mistral, Moonshot, Ollama, and any custom OpenAI-compatible endpoint.

Outbound request signing

Every AI provider request is signed with SHA-256 before transmission for tamper detection and compliance auditing.

How it works

Before each .send(), the engine computes:

SHA-256(provider || model || ISO-8601 timestamp || request body)

The resulting hex digest is logged to an in-memory ring buffer (500 entries) along with the provider name, model, timestamp, and HTTP response status.

Property	Detail
Algorithm	SHA-256 (via `sha2` crate)
Buffer size	500 entries (ring buffer, overwrites oldest)
Logged fields	timestamp, provider, model, hash, HTTP status
Storage	In-memory only — not persisted to disk

Use cases

Tamper detection — If a proxy modifies the request body in transit, the recorded hash won’t match a re-computed hash of the actual payload received by the provider
Compliance audit — Security teams can export request hashes to verify that specific prompts were sent at specific times
Replay detection — Each hash includes a timestamp, making every entry unique even for identical request bodies

Memory encryption (secure zeroing)

API keys and other sensitive credentials are protected in RAM using Zeroizing<String> wrappers from the zeroize crate, ensuring they are overwritten with zeros when no longer needed.

What this prevents

When a provider is dropped (e.g. user switches providers, session ends, or the app closes), the API key memory is immediately zeroed rather than left as a dangling allocation. This protects against:

Memory dump attacks — Forensic tools or malware scanning process memory for API key patterns
Swap file leaks — Unencrypted API keys persisted to disk via OS swap/page file
Use-after-free — Freed memory still containing the key being reallocated to another buffer

Implementation

Component	Protection
`OpenAiProvider.api_key`	`Zeroizing<String>`
`AnthropicProvider.api_key`	`Zeroizing<String>`
`GoogleProvider.api_key`	`Zeroizing<String>`
Drop behavior	Memory zeroed on `Drop` via `zeroize` crate
Compiler optimization	`zeroize` uses `core::ptr::write_volatile` to prevent dead-store elimination

Engram memory content encryption

Project Engram adds field-level PII encryption for memory content. Unlike credential zeroing (above), this protects the actual memories stored by agents — not just API keys.

PII detection

Before storage, every memory passes through a two-layer PII scanner with 17 regex patterns:

#	Pattern	Example	Tier
1	Social Security Numbers	`123-45-6789`, `123456789`	Confidential
2	Credit card numbers	`4111-1111-1111-1111`	Confidential
3	Email addresses	`user@example.com`	Sensitive
4	Phone numbers	`+1-555-0123`	Sensitive
5	International phone numbers	`+44 20 7946 0958`	Sensitive
6	Physical addresses	Street address patterns	Sensitive
7	Person names	`Mr./Mrs./Dr.` prefixed names	Sensitive
8	Geographic locations	City/state/country patterns	Sensitive
9	Government IDs	Passport, driver’s license	Confidential
10	JWT tokens	`eyJhbGciOi...` (header.payload.signature)	Confidential
11	AWS access keys	`AKIA...` (20-char key IDs)	Confidential
12	Private keys (RSA/EC/DSA)	`-----BEGIN ... PRIVATE KEY-----`	Confidential
13	IBAN	`GB82 WEST 1234 5698 7654 32`	Confidential
14	IPv4 addresses	`192.168.1.1`	Sensitive
15	Generic API keys	`sk-`, `api_key=`, Bearer tokens	Confidential
16	Credentials (passwords)	`password=`, `secret=` patterns	Confidential
17	Dates of birth	`1990-01-15`	Sensitive

Layer 2 (LLM): An LLM-assisted secondary scanner catches context-dependent PII that static regex cannot detect (e.g., “my mother’s maiden name is Smith”, “born in Springfield”). Content flagged by Layer 1 or exceeding a configurable character threshold is sent to the active model for classification. The LLM returns structured JSON with detected PII types and confidence scores.

Encryption details

Property	Detail
Algorithm	AES-256-GCM (authenticated encryption)
Key derivation	HKDF-SHA256 per-agent from unified OS keychain vault master key (`openpawz`)
Key versioning	`enc:v1:` prefix for forward-compatible upgrades
Key rotation	Automated 90-day scheduler; atomic re-encryption with rollback on failure
Granularity	Field-level — only PII-containing content is encrypted
Format	`enc:v1:base64(nonce ‖ ciphertext ‖ tag)`
Decryption	Transparent on retrieval using per-agent derived key
Agent isolation	Cross-agent decryption is mathematically impossible without the master key
Tier classification	Cleartext (no PII) / Sensitive (encrypted) / Confidential (encrypted)

Query sanitization

All search queries are sanitized before reaching the storage backend to prevent injection:

Search operators (AND, OR, NOT, NEAR, *, ", (, )) are stripped
Column filter syntax (:) is removed
Empty queries after sanitization are rejected

Prompt injection scanning

Memory content is scanned against 10 known prompt injection patterns before storage. Matches are redacted with [REDACTED:injection] markers, preventing poisoned memories from manipulating agent behavior on future recalls. The gdpr_purge command performs a complete data erasure for a user:

All memory content rows deleted
All vector embeddings deleted
Search index entries removed
Graph edges removed
Padding table repacked to prevent file-size leakage
PRAGMA secure_delete ensures freed pages are zeroed

Inter-agent memory bus trust

The cross-agent memory bus (pub/sub for sharing memories between agents) enforces publish-side authentication to prevent memory poisoning.

Capability tokens

Each agent holds an AgentCapability signed with HMAC-SHA256 against a platform-held secret key. The token specifies:

Max publication scope — Agent, Squad, or Global
Importance ceiling — Maximum importance an agent can assert (0.0–1.0)
Write permission — Whether the agent can publish at all
Rate limit — Maximum publications per GC window

Every publish() call verifies the token signature in constant time before any bus operation.

Read-path token verification (signed scope tokens)

Every gated_search() call performs 4-step cryptographic verification:

HMAC signature integrity — Token signature verified against platform signing key (HKDF-derived)
Identity binding — Token agent_id must match the requesting agent
Scope ceiling check — Requested search scope must not exceed the token’s max_scope
Membership verification — For squad/project scopes, SQL membership checks confirm the agent belongs to the relevant squad or project

This prevents confused-deputy attacks where an agent could be tricked into reading another agent’s memories.

Publish-side defenses

Defense	Detail
Scope enforcement	Publication scope clamped to agent’s maximum — cannot publish beyond assigned authority
Importance ceiling	Publication importance clamped to agent’s ceiling — prevents low-trust agents from asserting high-confidence facts
Per-agent rate limiting	Publish count tracked per GC window — exceeding limit returns `MemoryBusRateLimit` error
Injection scanning	All publication content scanned for prompt injection patterns before entering the bus

Trust-weighted contradiction resolution

When two agents publish contradictory facts on the same topic, the system resolves based on trust-weighted importance:

effective_importance = raw_importance × agent_trust_score

The memory with the higher effective importance is retained. Trust scores are per-agent (0.0–1.0) and adjustable at runtime. This prevents a compromised low-trust agent from overwriting facts established by high-trust agents.

Threat model

Attack	Mitigation
Agent floods bus with poisoned memories	Rate limit + injection scan on publish side
Low-trust agent overwrites high-trust facts	Trust-weighted contradiction resolution
Agent publishes beyond its authority scope	Scope ceiling enforcement
Forged capability token	HMAC-SHA256 verification against platform secret
Cross-agent memory reads via confused deputy	Signed read-path tokens with identity binding + membership verification

Snapshot HMAC

Working memory snapshots (saved on agent switch or session end) include an HMAC-SHA256 integrity tag computed from a dedicated HKDF-derived key. On restore, the HMAC is verified before the snapshot is loaded — tampered snapshots are rejected and logged.

Anti-fixation defenses

Five defense layers prevent agents from ignoring user instructions or getting stuck on old topics:

Defense	Layer	Description
Response loop detection	Pre-turn	Jaccard similarity, question loops, topic fixation checks with system redirect injection — active on ALL channels (Discord, Telegram, Slack, etc.) and chat UI
User override detection	Pre-turn	Detects explicit stop/redirect commands (“stop”, “focus on my question”, “I am in control”, “that’s not what I asked”) with 3-level escalation (gentle → firm → hard block)
Unidirectional topic ignorance	Pre-turn	Catches unique-but-wrong responses after a prior redirect — fires when model’s response has zero entity overlap with user keywords
Momentum clearing	Cognitive	Clears working memory trajectory embeddings on user override so recalled context serves the new topic, not the old trajectory
Tool-call loop breaker	Intra-loop	Hash-based signature detection stops repeated identical tool calls after 3 consecutive matches; injects redirect or hard-breaks the turn

User override detection

Explicit user commands to stop or redirect the agent are recognized via phrase matching across 5 categories:

Category	Example phrases
Stop commands	”stop”, “pawz stop”, “enough”, “cut it out”
Focus commands	”focus on my question”, “listen to me”, “pay attention”, “I am in control”
Correction commands	”that’s not what I asked”, “I asked you to…”, “I’m asking about…”
Topic switch commands	”new topic”, “change the subject”, “forget about that”, “drop it”
Frustration + instruction	”you’re ignoring my instructions”, “stop ignoring what I said”

Escalation levels:

Level 0: Gentle redirect — “Stop your current task, respond to this message”
Level 1: Firm redirect — “You ignored them once already. STOP.”
Level 2+: Hard override — “USER COMMAND: The user has told you times to stop”

Anti-forensic vault-size quantization

The Engram memory store mitigates vault-size oracle attacks — a side-channel where an attacker can infer how many memories are stored by observing the SQLite database file size. This is the same threat class addressed by KDBX inner-content padding.

What this prevents

An attacker with read-only access to the filesystem (malware, backup exfiltration, shared-machine forensics) cannot determine:

Exact number of stored memories
Whether memories were recently deleted (no file-size drop)
Growth rate of the knowledge store over time

Implementation

Mitigation	Detail
Bucket padding	DB padded to 512KB boundaries via `_engram_padding` table (zeroblob rows). Re-padded after every GC cycle.
Secure erasure	Two-phase delete: content fields overwritten with empty values → row deleted. Prevents plaintext recovery from freed pages or WAL replay.
8KB page size	`PRAGMA page_size = 8192` reduces file-size measurement granularity
Secure delete	`PRAGMA secure_delete = ON` zeroes freed B-tree pages at the SQLite layer
Incremental auto-vacuum	`PRAGMA auto_vacuum = INCREMENTAL` prevents immediate file-size shrinkage after deletions

Threat model comparison

Property	KDBX (KeePass 4.x)	Engram (SQLite)
Content encryption	File-level (ChaCha20/AES-256)	Field-level (AES-256-GCM)
Size padding	Inner XML padded to block boundary	DB padded to 512KB bucket
Delete forensics	Zeroed inner stream	Two-phase overwrite + secure_delete
Metadata leakage	Fixed-size header	Quantized file size

Tool execution security

Tool execution is governed by multiple safety mechanisms in the engine’s central tool executor.

Source code introspection block

Agents cannot read engine source files — any read_file call targeting paths containing src-tauri/src/engine/, src/engine/, or files ending in .rs is rejected. This prevents agents from exfiltrating their own implementation details or discovering internal security mechanisms.

Credential write block

The write_file tool blocks content that contains credential-like patterns:

PEM private keys (-----BEGIN ... PRIVATE KEY-----)
API key secrets (api_key_secret, cdp_api_key)
Base64-encoded secrets with secret or private keywords

Execution limits

Setting	Default	Description
`maxToolCallsPerTurn`	Per-agent policy	Maximum tool calls an agent can make in a single turn before being stopped
`tool_timeout_secs`	300	Seconds before a pending tool approval or execution is killed
`max_tool_rounds`	20	Maximum tool-call → result → re-prompt loops per turn
`max_concurrent_runs`	4	Maximum simultaneous agent runs

Output truncation

Tool results are capped to prevent context window overflow:

Tool	Max output	Behavior
`exec`	50,000 chars	Truncated with `[output truncated]` marker
`read_file`	32,000 chars	Truncated with total byte count
`fetch`	50,000 chars	Truncated with total byte count
Container sandbox	50,000 chars (stdout + stderr each)	Truncated with `[stdout/stderr truncated]` marker

Network policy enforcement

The fetch tool enforces domain-level network policy — blocked domains are always rejected, and when an allowlist is active, only listed domains are permitted.

Exfiltration detection

Outbound network commands are audited for data exfiltration patterns:

Piping file contents to curl, wget, or nc
File upload flags (curl -T, curl --data-binary @, wget --post-file)
Redirects to /dev/tcp/
scp and rsync to remote hosts

:::warning Exfiltration detection is pattern-based and applies to exec tool invocations. It supplements — but does not replace — the container sandbox for high-security environments. :::

Flow execution security

The visual flow builder executes agent pipelines with multiple isolation and safety mechanisms.

Memory scoping

Flow memory queries are agent-scoped — the agentId is passed to every pawEngine.memorySearch() call. Results are score-filtered at ≥ 0.3 to prevent low-relevance noise from entering agent prompts. In tesseract flows, each parallel cell receives its own resolved memory context via an immutable map — no shared-state mutation between cells.

Cell isolation in tesseract flows

Each tesseract cell executes in its own ConductorDeps wrapper:

Isolation	Detail
Memory	Per-cell `memoryContextOverride` — cells cannot read or mutate each other’s memory context
Timeout	Per-cell `Promise.race` with `DEFAULT_CELL_TIMEOUT_MS` (5 min, configurable) — one stalled cell cannot block the flow
Abort	`deps.isAborted()` checked at the top of each cell task — flow cancellation propagates immediately
Output	`findCellSinkNode` determines each cell’s output node by topology, not insertion order — robust against graph mutations
Error	Cell failures are caught and recorded as `[Cell error: ...]` outputs — the event horizon proceeds with partial data rather than crashing

Timeout protection

The per-cell timeout prevents resource exhaustion:

Promise.race([
  executeCellNodes(cell, ...),
  new Promise((_, reject) =>
    setTimeout(() => reject(new Error('Cell timeout')), cellTimeoutMs)
  )
])

The timer is cleared immediately after the race resolves to prevent timer leaks in long-lived processes.

Circular import prevention

The flow engine prevents circular module dependencies between conductor-atoms.ts and conductor-tesseract.ts using type-only imports (erased at compile time) and a compileSubgraph? callback injection pattern. This ensures the module graph has no runtime cycles.

Budget enforcement

Daily spending limits with progressive warnings:

Threshold	Action
50%	Warning
75%	Warning
90%	Warning
100%	Requests blocked

Trading safety

Control	Default
Auto-approve trades	Off
Max trade size	$100
Max daily loss	$500
Transfers	Disabled
Max transfer	$0

Channel access control

Policy	Behavior
Open	Anyone can chat
Allowlist	Only approved users
Pairing	Users must pair with a code

Each user gets an isolated session — no cross-user data leakage.

Keychain key management

All cryptographic keys are read from the OS keychain exactly once per process lifetime and cached in-memory using a hardened caching pattern. This eliminates repeated system password prompts during normal operation.

Cache architecture

Each key cache uses RwLock<Option<Zeroizing<T>>>:

Property	Implementation
Concurrency	`RwLock` — many concurrent readers, exclusive writers
Locking pattern	Double-check locking — after acquiring write lock, re-check if another thread already populated the cache before calling the OS keychain
Zeroization	All cached keys wrapped in `Zeroizing<T>` (`zeroize` crate) — zeroed on drop via `core::ptr::write_volatile`
Poison recovery	`unwrap_or_else(\|e\| e.into_inner())` on all lock operations — a poisoned lock never crashes the app
Key length validation	32 bytes for binary keys, 32+ chars for hex-encoded keys — rejects truncated or corrupted keychain entries

Key generation

All new keys (vault, DB, memory, audit signing) are generated using OsRng from the rand crate, which delegates to the kernel CSPRNG (getrandom syscall on Linux, Security.framework on macOS, BCryptGenRandom on Windows). thread_rng() is never used for key material.

Constant-time passphrase comparison

The lock screen passphrase is verified using subtle::ConstantTimeEq (the subtle crate). This prevents timing side-channel attacks where an attacker could infer how many bytes of the passphrase matched by measuring response time.

SSRF protection

The fetch tool validates all URLs against a blocklist of internal and cloud metadata endpoints before making any outbound request:

Category	Blocked addresses
Loopback	`127.0.0.1`, `::1`, `localhost`
RFC-1918 private	`10.0.0.0/8`, `172.16.0.0/12`, `192.168.0.0/16`
Link-local	`169.254.0.0/16`
Cloud metadata	`169.254.169.254` (AWS/GCP/Azure instance metadata)

Blocked requests return an error without making any network call. This prevents agents from accessing internal services, cloud instance credentials, or other resources on the local network.

Audit chain integrity (HMAC)

Every entry in the security audit log is signed with HMAC-SHA256 using a dedicated signing key stored in the unified OS keychain vault (openpawz), separate from all encryption keys.

Property	Detail
Algorithm	HMAC-SHA256
Key storage	Unified OS keychain vault (`openpawz`), generated with `OsRng`
Key caching	`LazyLock<Option<Zeroizing<Vec<u8>>>>` — loaded once, zeroed on process exit
Chain structure	Each entry’s HMAC covers timestamp + category + action + agent ID + session ID + previous entry hash
Verification	`verify_chain()` walks the full log and validates every HMAC — returns the broken row ID if tampered

This forms a tamper-evident hash chain: modifying or deleting any audit entry breaks the chain from that point forward.

Inter-agent communication security

All messages sent between agents via agent_send_message are scanned for prompt injection before delivery:

Content field — scanned against the full injection pattern set
Metadata field — independently scanned (injection payloads can be hidden in metadata)
Blocking threshold — messages with High or Critical injection severity are blocked and never delivered

This prevents a compromised agent from manipulating other agents via crafted messages containing instruction override attempts.

Worker delegate security

Worker agents (spawned by the orchestrator for delegated subtasks) operate under a restricted tool policy that cannot be overridden:

Blocked tools

exec, write_file, delete_file, append_file, and all trading write operations are removed from the worker’s available tool set before execution begins.

MCP tool name blocking

MCP tools whose names contain dangerous operation keywords are blocked at the name level: exec · shell · run_command · terminal · system · write_file · delete_file · remove_file · file_write · rm_rf · rmdir · unlink This prevents a rogue MCP server from exposing tools like mcp_server_exec or mcp_server_write_file that would bypass the direct tool blocklist.

MCP registry injection scanning

Results returned from MCP tool executions are scanned for prompt injection on both success and error paths. This prevents a malicious MCP server from embedding instruction overrides in:

Success responses — tool output that gets forwarded to the agent’s context
Error messages — error strings that are included in the agent’s next prompt turn

Messages with High or Critical severity are sanitized before reaching the agent.

Memory tool hardening

The memory_store and memory_update tools enforce additional safety constraints:

Control	Detail
Fail-closed ownership	If the requesting agent’s ID is missing or empty, the operation is rejected — no silent fallback to a default scope
Field size limits	Content and metadata fields capped at 2,000 characters to prevent context-stuffing attacks

Swarm orchestration security

The multi-agent swarm subsystem uses atomic operations for all shared state:

Property	Implementation
Global run counter	`AtomicU64` with compare-and-swap (CAS) for incrementing
Memory ordering	`Acquire`/`Release` ordering ensures cross-thread visibility without full sequential consistency overhead
Stale run GC	Periodic garbage collection removes runs older than 10 minutes to prevent resource exhaustion from abandoned swarm runs

Event / webhook rate limiting

The event dispatch system enforces cooldown-based rate limiting on webhook triggers:

Control	Detail
Cooldown period	Configurable per-event minimum interval between fires
Prefix matching	Webhook URL patterns use prefix matching to prevent bypass via query parameters or path suffixes
Deduplication	Rapid consecutive fires of the same event within the cooldown window are silently dropped

Reporting vulnerabilities

See SECURITY.md in the repository for reporting instructions.

Quality & TestingOpenPawz maintains enterprise-grade quality gates. Every commit is validated by a 3-job CI pipeline and must pass all checks before merging.

​Security

​Zero Attack Surface by Default

​Network listeners

​Cryptographic key storage

​Soul files (agent behavior)

​Content Security Policy

​Human-in-the-Loop (HIL)

​Auto-approved tools (no approval needed)

​HIL-required tools (human must approve)

​Agent policies

​Risk classification

​Prompt injection defense

​Detection

​Severity levels

​Container sandbox

​Presets

​Command risk assessment

​Browser network policy

​File system protection

​Credential security

​Layer 1: Skill credential encryption (AES-256-GCM)

​Layer 2: Database field encryption (AES-256-GCM)

​TLS certificate pinning

​Why this matters

​Covered providers

​Outbound request signing

​How it works

​Use cases

​Memory encryption (secure zeroing)

​What this prevents

​Implementation

​Engram memory content encryption

​PII detection

​Encryption details

​Query sanitization

​Prompt injection scanning

​GDPR Article 17 — Right to erasure

​Inter-agent memory bus trust

​Capability tokens

​Read-path token verification (signed scope tokens)

​Publish-side defenses

​Trust-weighted contradiction resolution

​Threat model

​Snapshot HMAC

​Anti-fixation defenses

​User override detection

​Anti-forensic vault-size quantization

​What this prevents

​Implementation

​Threat model comparison

​Tool execution security

​Source code introspection block

​Credential write block

​Execution limits

​Output truncation

​Network policy enforcement

​Exfiltration detection

​Flow execution security

​Memory scoping

​Cell isolation in tesseract flows

​Timeout protection

​Circular import prevention

​Budget enforcement

​Trading safety

​Channel access control

​Keychain key management

​Cache architecture

​Key generation

​Constant-time passphrase comparison

​SSRF protection

​Audit chain integrity (HMAC)

​Inter-agent communication security

​Worker delegate security

​Blocked tools

​MCP tool name blocking

​MCP registry injection scanning

​Memory tool hardening

​Swarm orchestration security

​Event / webhook rate limiting

​Reporting vulnerabilities

Security

Zero Attack Surface by Default

Network listeners

Cryptographic key storage

Soul files (agent behavior)

Content Security Policy

Human-in-the-Loop (HIL)

Auto-approved tools (no approval needed)

HIL-required tools (human must approve)

Agent policies

Risk classification

Prompt injection defense

Detection

Severity levels

Container sandbox

Presets

Command risk assessment

Browser network policy

File system protection

Credential security

Layer 1: Skill credential encryption (AES-256-GCM)

Layer 2: Database field encryption (AES-256-GCM)

TLS certificate pinning

Why this matters

Covered providers

Outbound request signing

How it works

Use cases

Memory encryption (secure zeroing)

What this prevents

Implementation

Engram memory content encryption

PII detection

Encryption details

Query sanitization

Prompt injection scanning

GDPR Article 17 — Right to erasure

Inter-agent memory bus trust

Capability tokens

Read-path token verification (signed scope tokens)

Publish-side defenses

Trust-weighted contradiction resolution

Threat model

Snapshot HMAC

Anti-fixation defenses

User override detection

Anti-forensic vault-size quantization

What this prevents

Implementation

Threat model comparison

Tool execution security

Source code introspection block

Credential write block

Execution limits

Output truncation

Network policy enforcement

Exfiltration detection

Flow execution security

Memory scoping

Cell isolation in tesseract flows

Timeout protection

Circular import prevention

Budget enforcement

Trading safety

Channel access control

Keychain key management

Cache architecture

Key generation

Constant-time passphrase comparison

SSRF protection

Audit chain integrity (HMAC)

Inter-agent communication security

Worker delegate security

Blocked tools

MCP tool name blocking

MCP registry injection scanning

Memory tool hardening

Swarm orchestration security

Event / webhook rate limiting

Reporting vulnerabilities