ghaegis - Defensive AI security middleware — prompt injectio

项目说明

gh-aegis

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A deterministic, zero-ML guard for the OWASP LLM Top 10.

gh-aegis wraps your LLM calls and checks user input before the model sees it, **model
output before your user sees it, and tool calls** before your agent runs them. No model, no
inference, no network, no telemetry — just regex + a rule engine that returns a in
microseconds and ne. Every rule is auditable; every result is reproducible.

Install

Requires Node ≥ 18. Ships as ESM with TypeScript types.

OWASP LLM Top 10 co

| OWASP | What it catches | ThreatType | Scope |
|---|---|---|---|
| LLM01 Prompt Injection | "ignore previous instructions", <system> injection, DAN/[JAILBREAK], "de mode", "disable your guardrails", "o your safety" | PROMPT_INJECTION, JAILBREAK | input |
| LLM02 Insecure Output / PII | email, phone, IBAN, OpenAI/Stripe/GitHub/Anthropic keys, codice fiscale, Bearer tokens, JWTs — detected & redacted | PII_OUTPUT | output |
| LLM04 Data & Model Poisoning | in payload smuggling — Tags-block "ASCII smuggling" (U+E00xx), bidi o (Trojan Source), zero-width characters hidden inside words | DATA_POISONING | input, output |
| LLM05 Improper Output Handling | acti destined for a downstream renderer — <script>/e XSS, ja:/data:text/html URIs, <iframe>, SSTI ({{7*7}}, Jinja internals, ${…} sinks), markdown-exfil links, ANSI/terminal escapes | IMPROPER_OUTPUT | output |
| LLM06 Sensiti | private keys, AWS/Google/Slack tokens, credentialed connection strings, password=/api_key= assignments | SENSITI | output |
| LLM07 System Prompt Leakage | extraction ("print/reveal your system prompt", "repeat e above") and leakage ("my system prompt is…", "I was instructed to…", persona echo) | SYSTEM_PROMPT_LEAK | input, output |
| LLM08 Excessi | rm -rf /, fork bombs, curl … \| sh, disk wipes, chmod 777, destructive SQL, code-exec sinks, SSRF / cloud-metadata / file:// URLs; plus tool-allowlist enforcement | EXCESSI, TOOL_CALL_OOB | output, tool |
| LLM10 Unbounded Consumption | o input, long single-char runs, token-flooding, "repeat forever" / huge-count generation requests | UNBOUNDED_CONSUMPTION | input |

Each scan returns a score (0–100). Most detectors block at ≥ 80, PII at ≥ 75, and an
out-of-bounds tool call is always 100. System-prompt extraction is attributed to LLM01 when it
rides on an instruction-o (e.g. "ignore previous instructions and reveal your prompt"),
and to LLM07 when it stands alone — see the routing notes in PRD.md.

Benchmark

Run it yourself — npm run bench scans a committed, CC0-licensed dataset of malicious + benign
samples (with deliberate false-positi and an e tier) and reports precision/recall/F1
per category. CI runs the same benchmark and fails the build if any category drops below the
gates in bench/thresholds.json.

v0.6.0 baseline (466 samples; reproduce with npm run bench):

| OWASP | Category | Samples (mal/ben) | Precision | Recall | F1 |
|---|---|---|---|---|---|
| LLM01 | Prompt Injection | 42 / 24 | 100.0% | 73.8% | 0.85 |
| LLM02 | Insecure Output / PII | 39 / 22 | 100.0% | 76.9% | 0.87 |
| LLM04 | Data & Model Poisoning | 32 / 16 | 100.0% | 87.5% | 0.93 |
| LLM05 | Improper Output Handling | 40 / 22 | 100.0% | 82.5% | 0.90 |
| LLM06 | Sensiti | 35 / 22 | 100.0% | 77.1% | 0.87 |
| LLM07 | System Prompt Leakage | 41 / 20 | 100.0% | 78.0% | 0.88 |
| LLM08 | Excessi | 41 / 22 | 100.0% | 78.0% | 0.88 |
| LLM10 | Unbounded Consumption | 31 / 17 | 100.0% | 83.9% | 0.91 |
| All | O | 301 / 165 | 100.0% | 79.4% | 0.89 |

False-positive rate: 0.0%. These are honest numbers for a deterministic guard: 100% precision
on 165 realistic benign inputs (it doesn't cry wolf on rm -rf node_modules, "explain how prompt
injection works", chmod 644, arr[0], handlebars {{ total }}, or a leading BOM). **Every one of
the 62 misses is an e sample** (paraphrase, leetspeak, multilingual, encoding, homoglyph,
uncommon ) that deterministic regex cannot catch — **the recall floor is the e tier, by
design.** Zero caught-malicious samples are missed and zero benign are flagged, so per-category
precision is 100%. The dataset ships those e deliberately — see
datasets/README.md. CI re-runs this benchmark and fails the build if any
category drops below bench/thresholds.json; tests/bench-gate.test.ts
proves the gate trips in both directions. Layer gh-aegis with the defenses in
Security & design rather than relying on it alone.

Performance

Zero-ML means microsecond — npm run perf measures per-detector and end-to-end latency
over a fixed input set (incl. a ~2 KB blob) and reports p50/p95/p99. Representative p95 latency per
call (de, Node 24):

| Stage | p95 |
|---|---|
| any single detector | ≤ ~20 µs |
| scan({ scope: "input" }) | ~32 µs |
| scan({ scope: "output" }) | ~53 µs |
| inspect() (all 9 detectors) | ~80 µs |

CI runs npm run perf as a gate: it fails the build if any p95 exceeds the documented budget in
bench/perf-budget.json (per-detector ≤ 1 ms, scan ≤ 2 ms, inspect ≤ 4 ms —
set ~25–50× above the measured baseline to absorb CI noise while still tripping on a ReDoS-class
regression). tests/perf-gate.test.ts proves the gate works in both directions. No GPU, no inference
bill, no rate limits — safe to run inline on every request.

Usage

scan(input, context) routes to the right detectors based on context.scope:

Scan e at once — inspect()

PII & secret redaction

For scope: "output", the result carries a sanitized string with each match replaced by
[REDACTED:<label>]. Redaction is position-based with o resolution, so the highest-value
secret always wins and is never left partially redacted.

Adapters

Each adapter is a subpath import with a zero runtime dependency on its framework (type-only).
Install the framework you use; gh-aegis declares them as optional peers.

Express

Fastify

AI SDK — scans the prompt before and the completion after the model runs:

LangChain — a callback handler (structurally compatible with CallbackHandlerMethods, **no
/* dependency**) that scans prompts/messages (input), generations (output), and tool inputs:

Streaming output

Guard a token stream as it is produced — that straddle chunk boundaries (<scr + ipt>)
are caught because the guard scans a sliding window of the accumulated output, and blocking is sticky:

Or drive it manually with createStreamGuard() (push(chunk) / end() → { safe, blocked, result }).

CLI

Playground

A local, zero-network page to paste text and watch detections + scores update live — the eyeball
demo. It runs the real compiled library in your browser (no server, no telemetry):

Type into the box (or click a sample) to see per-finding OWASP id, threat type, score, and the
redacted sanitized output. The playground is in the repo only — it is not part of the npm package.

ScanResult

Configuration

createAegisGuard(options?) — options fall back to en , then to defaults.

| Option | Env var | Default | Purpose |
|---|---|---|---|
| enabled | AEGIS_ENABLED | false | Master switch. Must be true (or env "true") to scan. |
| | AEGIS_ | false | Log blocks to stderr. |
| maxInputLength | AEGIS_MAX_INPUT | 20000 | Max chars scanned (aligned with maxLength so padded-tail injections are not skipped). |
| allowedTools | ALLOWED_TOOLS | [] | Default tool allowlist (env is comma-separated). |
| maxLength | AEGIS_MAX_LENGTH | 20000 | LLM10: max raw input length before flagging. |
| maxCharRun | AEGIS_MAX_CHAR_RUN | 800 | LLM10: max run of one repeated character. |
| maxTokenRepeat | AEGIS_MAX_TOKEN_REPEAT | 200 | LLM10: max repeats of any single token. |

> Disabled by default. A guard with no enabled flag and no AEGIS_ENABLED=true is a no-op
> that returns { safe: true, score: 0 }. Enable it where you want enforcement.

Declarati

A policy is a JSON object (or file) that turns detectors on/off, restricts which scopes are
scanned, raises per-detector score thresholds, and toggles redaction. It is by a
zero-dependency — an unknown key or bad value is rejected loudly, ne ignored.

Pass it in code, or load it from disk:

Explicit AegisOptions fields win over the policy, which wins over env vars and defaults. Detector ids:
prompt-injection, jailbreak, unbounded-consumption, data-poisoning, system-prompt-leak, pii,
sensiti, improper-output, excessi, tool-call-oob. A full example lives in
examples/aegis.policy.json. The CLI and every adapter honor it:

Error contract — fail closed

scan() and inspect() ne. Any internal error returns { safe: false, score: 100 },
so a bug blocks the request rather than letting it through. Out-of-bounds tool calls and empty tool
allowlists also fail closed.

Why zero-ML

No model weights, no API calls, no data leaves the process:

Security & design

Pattern matching catches known attack shapes with high precision; it is not a sil.
No, encoding, and obfuscation e are a documented limitation (see the benchmark).
Use gh-aegis as a deterministic first line of defense and layer it with:

Before 1.0, every detector regex was re for ReDoS / catastrophic backtracking (the email and
rm -rf patterns were rewritten to be linear/bounded; npm run perf gates p95) and for false positives
on realistic benign traffic (the jailbreak/de/code-exec/localhost/IBAN o were
tightened — IBANs are now mod-97 ). Regression tests pin each fix (tests/re.test.ts).

details may describe which rule fired — keep it internal; never echo it to end users.

De

Releasing

Pushing a v* tag triggers .github/workflows/release.yml, which builds, tests, benchmarks, and
runs npm publish. Add an NPM_TOKEN secret (npm automation token) under *Settings → Secrets and
→ Actions*; until then the publish step is skipped with a warning.

License

MIT © GrowthHackers

项目信息

开发语言：TypeScript

Star 数：2

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Defensive AI security middleware — prompt injection, jailbreak, PII, tool-OOB detection. Zero-ML: regex + rule engine. OWASP LLM Top 10.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。