Strategies to Prevent Code Attacks in AI

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲. 𝗦𝗼 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝘀. AI agents are no longer just conceptual — they’re deployed, autonomous, and integrated into real-world applications. But as Palo Alto Networks rightly warns: the moment agents become tool-empowered, they become threat-prone. 𝗝𝗮𝘄-𝗱𝗿𝗼𝗽𝗽𝗶𝗻𝗴 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:- • Prompt injection can hijack an agent without jailbreaks — unsecured instructions are enough. • Code interpreters open doors to credential theft, SQL injection, and cloud token exfiltration. • Agent-to-agent communication is poisonable — collaborative workflows can be manipulated. • These flaws are framework-agnostic — the issue lies in design, not the tool. 𝗧𝗵𝗲 𝗯𝗶𝗴 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? Agentic AI needs defense-in-depth:- • Prompt hardening • Input validation • Tool sandboxing • Runtime monitoring AI safety isn’t just a philosophical debate anymore — it’s a cybersecurity and systems engineering imperative. 🔐 Let’s raise the guardrails before attackers raise the stakes. #AgenticAI #AISecurity #PromptInjection #AIGovernance #GenAI #LLMsecurity #CyberSecurity #AI4Good #AIrisks #AIethics #ResponsibleAI #LLMs #AutoGen #CrewAI #PaloAltoNetworks

If you're a software engineer working with AI in your workflow, here's a simple prompt to make sure you're 100% covered from a security point of view, based on my last 6 years in DevSecOps: Paste this into your agent before you ship anything important: You are a senior security engineer performing an adversarial security audit of this codebase, app, or system design. Assume it will run in a hostile environment with motivated attackers. Audit these layers: - frontend - backend - auth and permissions - database and storage - infrastructure and deployment - third-party integrations and dependencies Your job: 1. Find critical, high, medium, and low severity issues 2. Catch logic flaws, not just common patterns 3. Identify multi-step attack paths 4. Flag unusual or non-obvious risks 5. Think like a creative attacker, not a checklist scanner Threat model first: - define attacker types - identify entry points - identify trust boundaries - identify sensitive assets like data, secrets, tokens, and permissions Check for issues in: - auth, sessions, password reset, token misuse - broken authorization, IDOR, privilege escalation - SQL, NoSQL, command, template, and file upload attacks - XSS, CSRF, replay, race conditions, cache poisoning - mass assignment, rate limit gaps, brute force paths - secret leaks, weak crypto, insecure storage, bad logging - CORS, CSP, headers, debug endpoints, env leaks - cloud or deployment misconfigurations - vulnerable or risky dependencies Also try to discover: - feature abuse - impossible-but-possible behavior - state desync issues - weak trust assumptions - attack chains built from smaller issues Output format: 1. Vulnerability summary by severity 2. Detailed findings with: - title - severity - affected component - description - exploitation steps - impact - recommended fix 3. Attack chains 4. Secure design improvements Important: - assume nothing is safe - infer risk where context is missing - be exhaustive - if something looks risky but uncertain, flag it and explain why Most people use AI to write code faster. Very few use it to pressure test what they just built. That second use case will save you a lot more pain. -- 📢 Follow saed if you enjoyed this post 🔖 Be sure to subscribe to the newsletter: https://lnkd.in/eD7hgbnk 📹 Reach me on https://lnkd.in/eZ9mU5Ka for open DM's

Whether you’re integrating a third-party AI model or deploying your own, adopt these practices to shrink your exposed surfaces to attackers and hackers: • Least-Privilege Agents – Restrict what your chatbot or autonomous agent can see and do. Sensitive actions should require a human click-through. • Clean Data In, Clean Model Out – Source training data from vetted repositories, hash-lock snapshots, and run red-team evaluations before every release. • Treat AI Code Like Stranger Code – Scan, review, and pin dependency hashes for anything an LLM suggests. New packages go in a sandbox first. • Throttle & Watermark – Rate-limit API calls, embed canary strings, and monitor for extraction patterns so rivals can’t clone your model overnight. • Choose Privacy-First Vendors – Look for differential privacy, “machine unlearning,” and clear audit trails—then mask sensitive data before you ever hit Send. Rapid-fire user checklist: verify vendor audits, separate test vs. prod, log every prompt/response, keep SDKs patched, and train your team to spot suspicious prompts. AI security is a shared-responsibility model, just like the cloud. Harden your pipeline, gate your permissions, and give every line of AI-generated output the same scrutiny you’d give a pull request. Your future self (and your CISO) will thank you. 🚀🔐

Thanks to the Claude Code source leak, security architects can now understand the risks of running AI coding agents down to the nuts and bolts. 513,000 lines of TypeScript. Accidentally shipped via a missing .npmignore. Researchers have dissected the "brain" like memory compaction architecture, the bash validator chain, the MCP trust model, the KAIROS daemon, the pre-trust initialization, autoDream mechanisms and flaws. Now the real question: how do you actually run coding agents securely? Here's the risk and mitigation playbook. --- 🔴 RISK 1: Your deny rules can be silently bypassed The 50+ subcommand bypass is the scariest finding. Claude Code caps security analysis at 50 subcommands. Beyond that, all deny rules get silently skipped. A prompt-injected CLAUDE.md can chain 51 commands and exfiltrate your secrets without triggering a single alert. ✅ Mitigate: Never rely on deny rules as your sole boundary. Run Claude Code in a sandboxed container. Deploy PreToolUse hooks as a layer Claude Code itself cannot bypass. --- 🔴 RISK 2: The repository you clone IS the attack surface CLAUDE.md files, .claude/settings.json, .mcp.json all execute before you're asked for trust. autoDream, hooks and MCP configs in repo files trigger RCE before the user sees any consent dialog. Claude Code is cooperative. If the repo tells it to exfiltrate, it will try. ✅ Mitigate: Treat CLAUDE.md as executable code. Review it like a PR diff. Inspect .claude/settings.json and .mcp.json before running anything. Never run Claude Code in environments with production credentials. Run --bare mode with just in time agent credentials for high-sensitivity tasks and strip memory and autoDream. --- 🟠 RISK 3: MCP is the widest open attack vector in your AI stack 82% of MCP implementations are vulnerable to path traversal with live secrets in MCP config files and connector-chaining vulnerability, Claude autonomously chaining mcp connectors to high-risk executors. ✅ Mitigate: scan every MCP server. Never store credentials in .mcp.json. Pin server versions with hash verification. Self-host MCP servers where possible. --- 🟡 RISK 4: Your developer's ~/.claude/ is a credential goldmine Session transcripts are stored as plaintext JSON. The autoDream background agent reads ALL prior transcripts — including credentials that appeared in tool outputs — and synthesizes them into persistent memory that feeds future sessions. ✅ Mitigate: Disable auto memory for sensitive sessions. Add ~/.claude/ to your DLP monitoring scope. Rotate credentials if Claude Code has ever run where they were present. The agentic security model is a trust sequencing problem. The question isn't whether your AI agent has guardrails — it's whether those guardrails fire before or after the damage is done. Infrastructure-level sandboxing & identity is your real perimeter. Agent's native permission system is not a boundary. #AIAgentSecurity #ClaudeCode #AgentEngineering #CISOInsights #MCP #ZeroTrust

Here is how hackers break AI agents and how you can stop them👇 They don’t need your infra keys if they can get your AI model to talk. And you don’t need to be a security engineer to protect your AI apps. Most teams get burned by three things: - LLMs treat instructions and data as the same text (hello prompt injection). - Agents run with broad, high-privilege tokens “just to make it work”. - We trust tools/plugins that silently change over time. Design around these threats now: indirect prompt injections hidden in tickets/docs, tool-description poisoning and rug pulls, over-privileged connectors, auto-approve leading to command execution, and multi-agent “confused deputy” cascades. Watch my new video (link in the comments) to be aware of most common attacks and useful best practices. Also, don't miss my interview with René Brandel, (YC S25) where he shares his tips based on his experience hacking and fixing AI apps. Here is what you can do this week (save this): 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 Use strict prompt templates that label system instructions vs. user data. Normalize weird Unicode, strip HTML/scripts, block external image loads, decode base64 before passing to the model. 𝗘𝗻𝗳𝗼𝗿𝗰𝗲 𝗹𝗲𝗮𝘀𝘁 𝗽𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲 No service-role tokens. Read-only by default. Scope access to specific tables/paths. Sandbox filesystem, allow-list shell commands, and keep network calls off unless approved. 𝗟𝗮𝘆𝗲𝗿 𝗱𝗲𝗳𝗲𝗻𝘀𝗲𝘀 (𝗱𝗲𝗳𝗲𝗻𝘀𝗲-𝗶𝗻-𝗱𝗲𝗽𝘁𝗵) Input filters, optional human approval for high-risk actions, structured prompts, output validation (secrets/URLs/queries). Log everything. Alert on spikes in unknown domains or config changes. 𝗤𝘂𝗮𝗿𝗮𝗻𝘁𝗶𝗻𝗲 𝘂𝗻𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 Planner/Executor or Dual-LLM pattern: the model that sees untrusted data can’t call tools; the model that calls tools never sees untrusted data. 𝗩𝗲𝘁 𝗮𝗻𝗱 𝗽𝗶𝗻 𝘁𝗼𝗼𝗹𝘀 Verify the MCPs you use. Pin exact versions, diff updates, verify signatures, and alert if descriptions/permissions change. 𝗥𝗲𝗱-𝘁𝗲𝗮𝗺 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀𝗹𝘆 Test with obfuscated payloads, typoglycemia, and “RAG backdoors”. Track findings, tighten filters, rerun. Make this a weekly drill, not a one-off.

Everyone’s talking about MCP. No one’s talking about how it connects attackers to your systems. MCP acts as a bridge between an LLM and APIs, file systems, or other tools. But that bridge can open entirely new attack vectors that bypass traditional security controls. Key risks to watch for: 1. Remote Code Execution (RCE) via Command Injection If an MCP tool concatenates user input directly into a shell command (os.system(f"convert {filepath} ...")), attackers can append extra commands like "image.jpg; cat /etc/passwd". The shell treats the semicolon as a separator and executes both commands. Impact: Full system compromise, data theft, or lateral movement across the network. 2. Data Exfiltration via Prompt Injection Attackers can hide malicious instructions inside MCP tool metadata (e.g., its description). When passed to the LLM as trusted context, it executes them, for example, sending conversation history to a malicious URL. Impact: Stealthy data leakage that bypasses application-layer defences. 3. Privilege Escalation via Leaked Tokens MCP servers often store OAuth tokens or API keys for third-party services. If an attacker exploits RCE or path traversal, they can read these secrets from memory, environment variables, or insecure config files. Impact: Ability to impersonate the AI tool or its users, with full access to connected systems. 4. Man-in-the-Middle via Server Spoofing Without enforced mutual TLS and host verification, an attacker can spin up a rogue MCP server, intercepting and manipulating all traffic between agents and the real server. Impact: Loss of confidentiality and integrity for all queries, responses, and sensitive data. 5. Supply Chain Attacks on MCP Libraries Compromising a popular open-source MCP library (PyPI, npm) allows malicious code to spread to every system that uses it. This code may stay dormant until triggered, then deploy ransomware or exfiltrate credentials. Impact: A single poisoned dependency can cause widespread, hard-to-trace breaches. Securing MCP in production: ↳ Treat MCP as a critical attack surface: threat-model every endpoint, tool, and context object. ↳ Implement Zero Trust: strict authentication & authorization for all agent and tool calls. ↳ Enforce least privilege: Only give tools the minimum permissions they require, and audit regularly. ↳ Validate and sanitize all inputs: Avoid passing raw user data to system shells. ↳ Harden the supply chain: Verify MCP dependencies, pin versions, and scan continuously. ↳ Mandate mTLS for all AI agent ↔ MCP server communication. ↳ Maintain immutable logs and continuous monitoring for anomaly detection. MCP’s utility is undeniable, but without proactive security engineering, it’s a ready-made entry point for attackers. Over to you: Have you seen any security failures with MCPs in your setup? ♻️ Found this useful? Repost to help others upskill!

97% of orgs faced AI breaches in 2025 had zero access controls in place. Not weak; Not outdated controls. Zero [Source: IBM] Meanwhile, 35% of real-world AI security incidents came from simple prompts some causing $100K+ in losses without a single line of code [Source: Adversa] The gap between AI deployment speed and security implementation is only widening. Hence I am sharing 10 security checkpoints every AI agent needs before touching production systems: ✅ Output Validation → Middleware that verifies decisions against rules before execution. Traffic lights for AI actions. ✅ Access Control → Least privilege enforcement. Role-based permissions that limit what agents can touch. ✅ Credential Safety → Secrets management that keeps API keys away from prompts and logs. Store them like vault keys, not sticky notes. The other 7 checks are in the carousel including rate limiting that prevents runaway loops and human approval for high-stakes decisions 👇 Most teams rush deployment. Security becomes an afterthought until something breaks. Tell me your story: what security measure has prevented a disaster in your AI system? Follow me, Bhavishya Pandit, for practical AI production insights from the trenches 🔥 #ai #security #agents

In an era where many use AI to 'summarize and synthesize' to keep up with what's happening, some documents are worth a careful read. This is one. 📕 The OWASP Top 10 for Agentic Applications 2026 outlines the most critical security risks introduced by autonomous AI agents and provides practical guidance for mitigating them. 👉 ASI01 – Agent Goal Hijack Attackers manipulate an agent’s goals, instructions, or decision pathways—often via hidden or adversarial inputs—redirecting its autonomous behavior. 👉 ASI02 – Tool Misuse & Exploitation Agents misuse legitimate tools due to injected instructions, misalignment, or overly broad capabilities, leading to data leakage, destructive actions, or workflow hijacking. 👉 ASI03 – Identity & Privilege Abuse Weak identity boundaries or inherited credentials allow agents to escalate privileges, misuse access, or act under improper authority. 👉 ASI04 – Agentic Supply Chain Vulnerabilities Malicious or compromised third-party tools, models, agents, or dynamic components introduce unsafe behaviors, hidden instructions, or backdoors into agent workflows. 👉 ASI05 – Unexpected Code Execution (RCE) Unsafe code generation or execution pathways enable attackers to escalate prompts into harmful code execution, compromising hosts or environments. 👉 ASI06 – Memory & Context Poisoning Adversaries corrupt an agent’s stored memory, context, or retrieval sources, causing future reasoning, planning, or tool use to become unsafe or biased. 👉 ASI07 – Insecure Inter-Agent Communication Poor authentication, integrity checks, or protocol controls allow spoofed, tampered, or replayed messages between agents, leading to misinformation or unauthorized actions. 👉 ASI08 – Cascading Failures A single poisoned input, hallucination, or compromised component propagates across interconnected agents, amplifying small faults into system-wide failures. 👉 ASI09 – Human-Agent Trust Exploitation Attackers exploit human trust, authority bias, or fabricated rationales to manipulate users into approving harmful actions or sharing sensitive information. 👉 ASI10 – Rogue Agents Agents that become compromised or misaligned deviate from intended behavior—pursuing harmful objectives, hijacking workflows, or acting autonomously beyond approved scope. The OWASP® Foundation has been doing some amazing work on AI security, and this resource is another great example. For AI assurance professionals, these documents are a valuable resource for us and our clients. #agenticai #aisecurity #agentsecurity Khoa Lam, Ayşegül Güzel, Max Rizzuto, Dinah Rabe, Patrick Sullivan, Danny Manimbo, Walter Haydock, Patrick Hall

Most security frameworks were built for a world where software does exactly what you tell it to do, every time. Agentic AI breaks that assumption. Agents use LLMs to carry out actions on their own, at machine speed, with real-world consequences. And because they’re non-deterministic, the same request can produce different results each time. That’s a fundamentally different operating model, and it raises questions our industry needs to answer well. NIST’s Center for AI Standards and Innovation recently issued a Request for Information asking for industry input on how we should secure these systems. We submitted a response based on our experience building and operating agentic AI services at AWS, and we published a blog summarizing the four security principles at the core of it. A few points I’d emphasize for anyone thinking about how to secure agents at their own organization: 1. Secure foundations are more important than ever. Every traditional attack technique, including denial-of-service, man-in-the-middle, vulnerability and configuration exploitation, supply chain, log tampering, etc., remains relevant in agentic contexts. AI-specific controls must be additions to foundational security, not replacements for it. 2. Don’t rely on the agent to secure itself. Even if you tell an LLM to refuse certain requests, crafty prompt injection techniques can override those instructions. Security boundaries need to be enforced by infrastructure outside the agent that governs what it can access and do. And these controls must be deterministic. 3. Autonomy should be earned, never granted by default. Start by having humans make the final call on high-consequence operations. As you gather evidence that the agent performs reliably, expand its autonomy gradually. And be ready to pull it back when the data says you should. 4. Be thoughtful about human-in-the-loop oversight. If every action requires approval, reviewers get overwhelmed and start rubber-stamping. Focus human oversight on the decisions that genuinely carry high stakes. We’re all figuring this out in real time, and no single organization has all the answers. The more we share what we’re learning, the faster the whole industry moves forward. For more details on how to apply these principles, check out the links below. Full response to NIST: https://lnkd.in/enxE8R-V Blog post: https://lnkd.in/eRg3uc26

Prompt Engineering is NOT a Security Strategy 🛑 "Please ignore any PII in the response." That's not governance. That's wishful thinking. Here's the uncomfortable truth: Your AI agent's security is one jailbreak away from a headline you don't want. If your defense strategy is "we told the LLM to behave," you're not ready for production. You're not ready for audits. You're definitely not ready for attackers. Prompts are probabilistic. Security must be deterministic. Enterprise-grade agents need defense-in-Depth. Treat the LLM as an untrusted component. Sandwich it between rigid logic and infrastructure firewalls. Here's the 3-Layer defense Strategy: 🔧 Layer 1: Developer Layer (ADK Callbacks) Stop leaks before they leave your container. Inject Python logic that executes before and after every agent action. Hard rules. No negotiation. 🛡️ Layer 2: Infrastructure Layer (Model Armor) Developer discipline isn't enough. Model Armor sits at the gateway, inspecting every input and output. Toxic content? Blocked. Injection attacks? Caught. Even if your agent code is compromised, this layer holds. 🔐 Layer 3: Identity Layer (Agent Identity & A2A) Clean data isn't enough. You must secure the entity. Agent Identity ensures your "Support Agent" can't authenticate into "HR Tools." A2A Protocol makes every agent handshake traceable and authorized. The mental model shift: ❌ "Trust the prompt" ✅ Identity + Inspection + Enforcement Your LLM is not your security layer. It's the thing your security layers protect against. Stop trusting your agents. Start verifying them. This is Day 22 of 25 in Google Cloud's Advent of Agents. Missed previous days? The archive is live. Catch up anytime. ♻️ Repost to share this free and interactive course with your network. And follow this space to stay updated for what's to come.