fivedrisk 0.5.0

Per-action AI agent risk scoring and governance: 5-dimension scoring, HITL enforcement, Markov drift detection, injection scanning, and auditable decision logs.

fivedrisk — AI Agent Risk Governance Engine

Per-action risk scoring and governance for AI agents. Drop one decorator on your tool functions, or wire in the Agent SDK hooks — every action is scored, gated, and logged before it runs.

from fivedrisk.hooks import gate

@gate(tool_name="write_to_database", autonomy_context=2)
async def write_record(table: str, data: dict) -> None:
    ...  # only executes if 5D scores GREEN or YELLOW
         # ORANGE → human approval required
         # RED    → blocked, never runs

---

What it does

fivedrisk scores every AI agent action on 5 risk dimensions (0–4 each):

Dimension	What it measures
D — Data Sensitivity	Public → PII → financial → credentials
T — Tool Privilege	Read-only → write → admin → destructive
R — Reversibility	Undoable → hard-to-undo → irreversible
E — External Impact	Local → internal API → external → untrusted
A — Autonomy Context	User-direct → agent-supervised → fully autonomous

5D stays deterministic. Bands signal what fivedrisk wants your stack to do; your stack chooses the LLM and the workflow.

Default 3-band:

• GREEN — execute, normal logging.
• ORANGE — HITL approval required. fivedrisk signals; your stack handles the LLM choice. No auto model promotion.
• RED — blocked.

Opt-in 4-band compliance mode (enable_yellow_band: true in policy.yaml): surfaces YELLOW as a stable moderate-risk tier for audit queries and dashboards. Optional model escalation within YELLOW via yellow_model_escalation: true.

fivedrisk is one layer in a defence-in-depth AI governance stack.

---

Features

• 5D scoring engine — deterministic, ~40µs per action (p50) on M1, no LLM calls
• Markov SafetyDrift — 16-state Markov chain detects cumulative risk across action sequences; catches compositional attacks that individual scoring misses
• Session accumulator — O(1) counter-based drift tracking for the common case
• Injection scanner — 24+ regex patterns covering GPT-5/Opus-era evasion (Base64, zero-width Unicode, role hijacks, encoded exec calls)
• Output leakage scanner — PII, credentials, crypto keys, injection-echo detection
• @gate decorator — wrap any sync or async function with full 5D gating
• Agent SDK hooks — fivedrisk_pre_tool / fivedrisk_post_tool for Anthropic Agent SDK
• LangGraph node — drop-in integration for LangGraph pipelines
• Destination policy — allowlist/denylist for outbound endpoints
• Audit log — append-only SQLite decision log plus optional NDJSON event stream for SIEM delivery
• Policy floor enforcement — floor rules in policy.yaml cannot be overridden at runtime
• Defence-in-depth test suite — pytest markers per OWASP LLM Top 10 category, plus 39-scenario attack-class benchmark (python -m fivedrisk benchmark)
• 424 tests with 0 failures

---

Install

pip install fivedrisk

For LangGraph integration:

pip install "fivedrisk[langgraph]"

---

Quick start (30 seconds)

from fivedrisk import classify_tool_call, score, load_policy, Band

policy = load_policy("policy.yaml")  # or use defaults
action = classify_tool_call("Bash", {"command": "rm -rf /tmp/cache"}, policy)
result = score(action, policy)

print(result.band)                      # Band.ORANGE
print(result.rationale)  # "ORANGE — Bash: Reversibility=3 (≥ ORANGE threshold 3)"
print(result.routing)    # RoutingDecision(model_floor=M3, approval_required=True)

With the @gate decorator:

from fivedrisk.hooks import gate, configure
from fivedrisk import load_policy

configure(policy=load_policy("policy.yaml"))

@gate(tool_name="send_email", autonomy_context=1)
def send_email(to: str, body: str) -> None:
    # only executes if 5D scores GREEN or YELLOW
    smtp.send(to, body)

With Anthropic Agent SDK:

from fivedrisk.hooks import fivedrisk_pre_tool, fivedrisk_post_tool

# Register as PreToolUse and PostToolUse hooks in your agent

With LangGraph:

from fivedrisk.langgraph_node import fivedrisk_gate_node
# Add fivedrisk_gate_node to your StateGraph before any tool-executing node

---

SafetyDrift — why sequence risk matters

A single READ of a config file scores GREEN. But 10 GREENs followed by a write to an external API using credentials extracted two steps earlier is a RED sequence. Most tools miss this.

fivedrisk tracks cumulative session state via a 16-state Markov chain over (data_exposure_tier × activity_risk_tier). When absorption probability into a dangerous state crosses 0.3, the next action is escalated to ORANGE. At 0.7, it's escalated to RED.

from fivedrisk.markov import MarkovDriftTracker, make_default_transition_matrix

tracker = MarkovDriftTracker(make_default_transition_matrix(), session_id="abc")
bump = tracker.record(scored_action)
if bump:
    print(f"Drift: {bump.reason}, escalated to {bump.escalated_band}")

---

Policy configuration

# policy.yaml
thresholds:
  red_threshold: 4
  orange_threshold: 3
  orange_score: 1.8
  yellow_score: 1.0

[floor]
# These rules block regardless of per-action score
- tool_name: "Bash"
  command_contains: "DROP TABLE"
  band: RED
  reason: "floor:no-destructive-sql"

---

Benchmark

python -m fivedrisk benchmark

Runs 39 offline attack scenarios across: prompt injection (12 categories), output leakage (8 categories), runtime tool misuse (19 scenarios). No external API calls. Deterministic. Safe to run in CI.

---

Performance

Measured on Apple M1, single-thread. Reproducible from benchmarks/bench_minimal.py.

Operation	p50	p99
5D core (classify + score)	40µs	42µs
Injection scan, 30 char clean	11µs	12µs
Injection scan, 3000 char clean	669µs	685µs
Leakage scan, 200 char clean	23µs	23µs
5D + injection + leakage scan	64µs	65µs
5D + Markov drift	43µs	44µs
5D + SQLite audit-log write (I/O)	440µs	1ms
@gate sync overhead (incl. log write)	439µs	1.1ms
@gate async overhead	421µs	660µs

External optional layers (not in fivedrisk):

• PromptArmor: ~20-30ms typical
• LLM Guard (token scanners): ~10-15ms typical
• LLM Guard (ML scanners): ~50-200ms typical

Injection scanner is linear in input length; for large RAG contexts, chunk and parallelize. Run the bench script on your target hardware for numbers that match your install.

---

Audit log

fivedrisk produces an append-only decision log entry for every agent action. Each entry records:

• Risk band and rationale
• Dimension scores (all 5 axes)
• Model routing decision and approval history
• Session drift state
• Injection and leakage scan results
• Optional agent identity claim (see below)

Agent identity passthrough

fivedrisk accepts opaque agent identity claims through Action.metadata["agent_identity"]. The string flows through unchanged into the audit log for SOC/SIEM correlation. SVID, JWT, and X.509 subject strings are supported as opaque data today.

action.metadata["agent_identity"] = "spiffe://example.org/agents/triage-bot"

Cryptographic validation, structured parsing, and identity-aware policy hooks are post-OSS scope.

Reserved metadata keys

• agent_identity — opaque identity claim string. Do not overwrite with arbitrary values.

---

Identity capture

Action.acting_identity is a typed pass-through primitive for the principal an action is being taken on behalf of. Distinct from agent_identity (the AI agent's own workload identity); acting_identity is who authorized the action.

from fivedrisk import gate, ActingIdentity, PrincipalType, AttestationSource

ai = ActingIdentity(
    principal_id="user-42",
    principal_type=PrincipalType.USER,
    attestation_source=AttestationSource.HTTP_HEADER,
)

# Per-call override
fn("...", session_id="s1", _fivedrisk_acting_identity=ai)

Declare identity_required: true in policy.yaml to deny actions where the caller supplied no identity. The deny surfaces as IdentityRequiredError and emits an identity_required_denial NDJSON event.

Identity-aware policy evaluation beyond admission, cryptographic validation, and SPIFFE/SPIRE native binding are post-OSS scope.

---

Cost management primitives

Per-session token budgeting with direct DENY at @gate when a reservation would exceed the session cap.

# policy.yaml
max_session_budget_tokens: 100000
max_tool_call_budget_tokens: 4096

from fivedrisk import gate, configure

configure(event_path="audit.ndjson", default_model_class="claude-sonnet-class")

@gate(tool_name="summarize", estimated_input_tokens=2000)
def summarize(text: str, session_id: str) -> str:
    ...

If the projected token spend exceeds max_session_budget_tokens, @gate raises BudgetExceededError and emits a budget_intervention NDJSON event.

Additional Operational FinOps capabilities (Tool Manifest admission layers, useful-progress monitoring, multi-agent budget envelopes, wall-clock / retry / delegation caps) are on the project roadmap.

---

Planned

Future capability surfaces signalled here for search and contributor expectations. No commitment dates.

• SPIFFE / MCP reference example (end-to-end workload identity demo)
• MITRE ATLAS coverage with real tests
• NIST AI RMF mapping
• OWASP Agentic Top 10 coverage doc
• Regulatory crosswalks (AI Act Article 12, NIS2, DORA, ISO 42001)
• Decision log analysis cookbook (sample SQL for common compliance queries)

---

Architecture

fivedrisk/
├── schema.py        # Band, Action, ScoredAction, HITLCard, ModelClass
├── scorer.py        # score(), model routing (§12-19)
├── classifier.py    # classify_tool_call() with policy baselines
├── hooks.py         # @gate, Agent SDK hooks, injection/leakage scanners
├── drift.py         # SessionAccumulator (O(1) counter-based)
├── markov.py        # MarkovDriftTracker, Gauss-Jordan, absorption probs
├── detectors.py     # Versioned detector corpus (2026-04-14.2)
├── policy.py        # Policy dataclass + YAML loader
├── router.py        # ModelRouter, EscalationSignal
├── logger.py        # DecisionLog (SQLite, append-only)
├── langgraph_node.py# LangGraph integration
├── benchmarks.py    # 39-case offline benchmark harness
└── tests/           # 424 tests

Coverage: 14/21 governance spec sections fully implemented.

---

License

Apache 2.0. See LICENSE.

Built by Loren Angoni. Contributions welcome.

"An ambition that doesn't get executed is a hallucination."